From 42e2b684c87edb0a7e45a8e3ffff8d88a311845c Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Sat, 15 Mar 2025 10:05:04 -0500
Subject: [PATCH 1/6] Add inference UI links

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 887db19..9ffa2a4 100644
--- a/README.md
+++ b/README.md
@@ -24,7 +24,7 @@ This is the official codebase of the paper
 
 **FlowDock: Geometric Flow Matching for Generative Protein-Ligand Docking and Affinity Prediction**
 
-\[[arXiv](https://arxiv.org/abs/2412.10966)\]
+\[[arXiv](https://arxiv.org/abs/2412.10966)\] \[[Neurosnap](https://neurosnap.ai/service/FlowDock)\] \[[Tamarind Bio](https://app.tamarind.bio/tools/flowdock)\]
 
 <div align="center">
 

From c11bcc696c285153f304a713f6f999c47682d9e0 Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Sat, 15 Mar 2025 16:19:48 -0500
Subject: [PATCH 2/6] Update DockGen plots

---
 ...structure_prediction_relaxed_bar_chart.png | Bin 683844 -> 1004731 bytes
 ...tructure_prediction_results_plotting.ipynb |  18 +++++++++++++++---
 2 files changed, 15 insertions(+), 3 deletions(-)

diff --git a/notebooks/dockgen_structure_prediction_relaxed_bar_chart.png b/notebooks/dockgen_structure_prediction_relaxed_bar_chart.png
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diff --git a/notebooks/dockgen_structure_prediction_results_plotting.ipynb b/notebooks/dockgen_structure_prediction_results_plotting.ipynb
index e7b02fa..eadcda8 100644
--- a/notebooks/dockgen_structure_prediction_results_plotting.ipynb
+++ b/notebooks/dockgen_structure_prediction_results_plotting.ipynb
@@ -497,7 +497,9 @@
     "    y=\"rmsd\",\n",
     "    hue=\"post-processing\",\n",
     "    # ignore outliers for better readability\n",
-    "    data=combined_relaxed_data[combined_relaxed_data[\"rmsd\"] < 10],\n",
+    "    data=combined_relaxed_data[\n",
+    "        (combined_relaxed_data[\"rmsd\"] < 10) & ~(combined_relaxed_data[\"rmsd\"].isna())\n",
+    "    ],\n",
     "    split=True,\n",
     "    inner=\"quartile\",\n",
     "    palette=colors,\n",
@@ -555,11 +557,21 @@
     "    num_methods = len(dockgen_labels)\n",
     "\n",
     "    num_dockgen_data_points = max(\n",
-    "        len(dockgen_results_table[(dockgen_results_table[\"method\"] == method)])\n",
+    "        len(\n",
+    "            dockgen_results_table[\n",
+    "                (dockgen_results_table[\"method\"] == method)\n",
+    "                & ~(dockgen_results_table[\"rmsd\"].isna())\n",
+    "            ]\n",
+    "        )\n",
     "        for method in dockgen_labels\n",
     "    )\n",
     "    num_dockgen_relaxed_data_points = max(\n",
-    "        len(dockgen_relaxed_results_table[(dockgen_relaxed_results_table[\"method\"] == method)])\n",
+    "        len(\n",
+    "            dockgen_relaxed_results_table[\n",
+    "                (dockgen_relaxed_results_table[\"method\"] == method)\n",
+    "                & ~(dockgen_relaxed_results_table[\"rmsd\"].isna())\n",
+    "            ]\n",
+    "        )\n",
     "        for method in dockgen_labels\n",
     "    )\n",
     "\n",

From 50e07465205508a27b7282ab6062ed6693a708cc Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Sun, 16 Mar 2025 18:31:00 -0500
Subject: [PATCH 3/6] Update Figure 1

---
 img/FlowDock.png | Bin 2485515 -> 2606492 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)

diff --git a/img/FlowDock.png b/img/FlowDock.png
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From 7343b20b5032b2834755586f529986d69b0347ce Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Fri, 21 Mar 2025 15:16:37 -0500
Subject: [PATCH 4/6] Add 0.0.3 revisions in bulk

---
 .env.example                                  |     2 +-
 .pre-commit-config.yaml                       |    16 +-
 Dockerfile                                    |     7 +-
 README.md                                     |    23 +-
 configs/data/plinder.yaml                     |    18 +
 environments/flowdock_environment.yaml        |   850 +-
 environments/flowdock_environment_docker.yaml |    76 +-
 flowdock/data/components/plinder_dataset.py   |   337 +
 flowdock/data/plinder_datamodule.py           |   280 +
 flowdock/utils/data_utils.py                  |     5 +-
 notebooks/casp16_flowdock_bust_results.csv    |   302 +
 .../casp16_flowdock_bust_results_relaxed.csv  |   302 +
 ...re_prediction_results_comparison_n=126.png |   Bin 0 -> 45521 bytes
 ...re_prediction_results_comparison_n=301.png |   Bin 0 -> 55754 bytes
 ...tructure_prediction_results_plotting.ipynb |   205 +
 .../casp16_multicom_ligand_pose_results.csv   | 30222 ++++++++++++++++
 ...structure_prediction_relaxed_bar_chart.png |   Bin 1004731 -> 1005529 bytes
 ...re_prediction_relaxed_rmsd_violin_plot.png |   Bin 344402 -> 356396 bytes
 ...tructure_prediction_results_plotting.ipynb |    35 +-
 ...rs_benchmark_mol_to_protein_mapping_1.json |     1 +
 ...rs_benchmark_mol_to_protein_mapping_2.json |     1 +
 ...rs_benchmark_mol_to_protein_mapping_3.json |     1 +
 ...ters_benchmark_protein_grouped_mols_1.json |     1 +
 ...ters_benchmark_protein_grouped_mols_2.json |     1 +
 ...ters_benchmark_protein_grouped_mols_3.json |     1 +
 ...diction_chemical_similarity_analysis.ipynb |   723 +
 ...structure_prediction_relaxed_bar_chart.png |   Bin 1170262 -> 1147928 bytes
 ...re_prediction_relaxed_rmsd_violin_plot.png |   Bin 383753 -> 384280 bytes
 ...on_relaxed_rmsd_violin_plot_by_protein.png |   Bin 0 -> 231416 bytes
 ...tructure_prediction_results_plotting.ipynb |    37 +-
 scripts/esmfold_prior_plinder_finetuning.sh   |    64 +
 scripts/esmfold_prior_tiered_training.sh      |     3 +
 scripts/esmfold_prior_training.sh             |     3 +
 scripts/harmonic_prior_training.sh            |     3 +
 scripts/plinder_download.sh                   |    38 +
 35 files changed, 33108 insertions(+), 449 deletions(-)
 create mode 100644 configs/data/plinder.yaml
 create mode 100644 flowdock/data/components/plinder_dataset.py
 create mode 100644 flowdock/data/plinder_datamodule.py
 create mode 100644 notebooks/casp16_flowdock_bust_results.csv
 create mode 100644 notebooks/casp16_flowdock_bust_results_relaxed.csv
 create mode 100644 notebooks/casp16_flowdock_vs_multicom_ligand_multi_ligand_structure_prediction_results_comparison_n=126.png
 create mode 100644 notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_comparison_n=301.png
 create mode 100644 notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb
 create mode 100644 notebooks/casp16_multicom_ligand_pose_results.csv
 create mode 100644 notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json
 create mode 100644 notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json
 create mode 100644 notebooks/posebusters_benchmark_mol_to_protein_mapping_3.json
 create mode 100644 notebooks/posebusters_benchmark_protein_grouped_mols_1.json
 create mode 100644 notebooks/posebusters_benchmark_protein_grouped_mols_2.json
 create mode 100644 notebooks/posebusters_benchmark_protein_grouped_mols_3.json
 create mode 100644 notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb
 create mode 100644 notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png
 create mode 100644 scripts/esmfold_prior_plinder_finetuning.sh
 create mode 100644 scripts/plinder_download.sh

diff --git a/.env.example b/.env.example
index a790e32..3a206c5 100644
--- a/.env.example
+++ b/.env.example
@@ -3,4 +3,4 @@
 # .env is loaded by train.py automatically
 # hydra allows you to reference variables in .yaml configs with special syntax: ${oc.env:MY_VAR}
 
-MY_VAR="/home/user/my/system/path"
+PLINDER_MOUNT="$(pwd)/data/PLINDER"
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 42ca94a..3de51e1 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -18,18 +18,18 @@ repos:
       - id: check-toml
       - id: check-case-conflict
       - id: check-added-large-files
-        args: ["--maxkb=10000"]
+        args: ["--maxkb=20000"]
 
   # python code formatting
   - repo: https://github.com/psf/black
-    rev: 24.10.0
+    rev: 25.1.0
     hooks:
       - id: black
         args: [--line-length, "99"]
 
   # python import sorting
   - repo: https://github.com/PyCQA/isort
-    rev: 5.13.2
+    rev: 6.0.1
     hooks:
       - id: isort
         args: ["--profile", "black", "--filter-files"]
@@ -43,7 +43,7 @@ repos:
 
   # python docstring formatting
   - repo: https://github.com/myint/docformatter
-    rev: v1.7.5
+    rev: eb1df347edd128b30cd3368dddc3aa65edcfac38 # Don't autoupdate until https://github.com/PyCQA/docformatter/issues/293 is fixed
     hooks:
       - id: docformatter
         args:
@@ -73,7 +73,7 @@ repos:
 
   # python check (PEP8), programming errors and code complexity
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.1.1
+    rev: 7.1.2
     hooks:
       - id: flake8
         args:
@@ -87,7 +87,7 @@ repos:
 
   # python security linter
   - repo: https://github.com/PyCQA/bandit
-    rev: "1.8.2"
+    rev: "1.8.3"
     hooks:
       - id: bandit
         args: ["-s", "B101"]
@@ -108,7 +108,7 @@ repos:
 
   # md formatting
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.21
+    rev: 0.7.22
     hooks:
       - id: mdformat
         args: ["--number"]
@@ -121,7 +121,7 @@ repos:
 
   # word spelling linter
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.3.0
+    rev: v2.4.1
     hooks:
       - id: codespell
         args:
diff --git a/Dockerfile b/Dockerfile
index d96d319..55c0c30 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -20,13 +20,16 @@ RUN mkdir -p /software/flowdock
 WORKDIR /software/flowdock
 
 ## Clone project
-RUN git clone https://github.com/BioinfoMachineLearning/FlowDock /software/flowdock 
+RUN git clone https://github.com/BioinfoMachineLearning/FlowDock /software/flowdock
 
 ## Create conda environment
 # RUN conda env create -f environments/flowdock_environment.yaml
 COPY environments/flowdock_environment_docker.yaml /software/flowdock/environments/flowdock_environment_docker.yaml
 RUN conda env create -f environments/flowdock_environment_docker.yaml
 
+# Install ProDy without NumPy dependency
+RUN python -m pip install --no-cache-dir --no-dependencies prody==2.4.1
+
 ## Automatically activate conda environment
 RUN echo "source activate flowdock" >> /etc/profile.d/conda.sh && \
     echo "source /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \
@@ -34,4 +37,4 @@ RUN echo "source activate flowdock" >> /etc/profile.d/conda.sh && \
 
 ## Default shell and command
 SHELL ["/bin/bash", "-l", "-c"]
-CMD ["/bin/bash"]
\ No newline at end of file
+CMD ["/bin/bash"]
diff --git a/README.md b/README.md
index 9ffa2a4..5f6d70f 100644
--- a/README.md
+++ b/README.md
@@ -76,6 +76,7 @@ cd FlowDock
 mamba env create -f environments/flowdock_environment.yaml
 conda activate FlowDock  # NOTE: one still needs to use `conda` to (de)activate environments
 pip3 install -e . # install local project as package
+pip3 install prody==2.4.1 --no-dependencies  # install ProDy without NumPy dependency
 ```
 
 Download checkpoints
@@ -159,6 +160,16 @@ mv pdb_2021aug02/ pdbsidechain/
 cd ../
 ```
 
+Lastly, to finetune `FlowDock` using the `PLINDER` dataset, one must first prepare this data for training
+
+```bash
+# fetch PLINDER data (NOTE: requires ~1 hour to download and ~750G of storage)
+export PLINDER_MOUNT="$(pwd)/data/PLINDER"
+mkdir -p "$PLINDER_MOUNT" # create the directory if it doesn't exist
+
+plinder_download -y
+```
+
 ### Generating ESM2 embeddings for each protein (optional, cached input data available on SharePoint)
 
 To generate the ESM2 embeddings for the protein inputs,
@@ -260,10 +271,10 @@ python flowdock/train.py experiment=flowdock_fm
 python flowdock/train.py experiment=flowdock_fm trainer.max_epochs=20 data.batch_size=8
 ```
 
-For example, override parameters to finetune `FlowDock`'s pretrained weights using a new dataset
+For example, override parameters to finetune `FlowDock`'s pretrained weights using a new dataset such as [PLINDER](https://www.plinder.sh/)
 
 ```bash
-python flowdock/train.py experiment=flowdock_fm data=my_new_datamodule ckpt_path=checkpoints/esmfold_prior_paper_weights.ckpt
+python flowdock/train.py experiment=flowdock_fm data=plinder ckpt_path=checkpoints/esmfold_prior_paper_weights.ckpt
 ```
 
 </details>
@@ -277,7 +288,7 @@ To reproduce `FlowDock`'s evaluation results for structure prediction, please re
 To reproduce `FlowDock`'s evaluation results for binding affinity prediction using the PDBBind dataset
 
 ```bash
-python flowdock/eval.py data.test_datasets=[pdbbind] ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt trainer=gpu
+python flowdock/eval.py data.test_datasets=[pdbbind] ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt trainer=gpu
 ... # re-run two more times to gather triplicate results
 ```
 
@@ -353,13 +364,13 @@ jupyter notebook notebooks/casp16_binding_affinity_prediction_results_plotting.i
 For example, generate new protein-ligand complexes for a pair of protein sequence and ligand SMILES strings such as those of the PDBBind 2020 test target `6i67`
 
 ```bash
-python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='YNKIVHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNKLEGKVPMHKLFLEMLEAKV' input_ligand='"c1cc2c(cc1O)CCCC2"' input_template=data/pdbbind/pdbbind_holo_aligned_esmfold_structures/6i67_holo_aligned_esmfold_protein.pdb sample_id='6i67' out_path='./6i67_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
+python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='YNKIVHLLVAEPEKIYAMPDPTVPDSDIKALTTLCDLADRELVVIIGWAKHIPGFSTLSLADQMSLLQSAWMEILILGVVYRSLFEDELVYADDYIMDEDQSKLAGLLDLNNAILQLVKKYKSMKLEKEEFVTLKAIALANSDSMHIEDVEAVQKLQDVLHEALQDYEAGQHMEDPRRAGKMLMTLPLLRQTSTKAVQHFYNKLEGKVPMHKLFLEMLEAKV' input_ligand='"c1cc2c(cc1O)CCCC2"' input_template=data/pdbbind/pdbbind_holo_aligned_esmfold_structures/6i67_holo_aligned_esmfold_protein.pdb sample_id='6i67' out_path='./6i67_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
 ```
 
 Or, for example, generate new protein-ligand complexes for pairs of protein sequences and (multi-)ligand SMILES strings (delimited via `|`) such as those of the CASP15 target `T1152`
 
 ```bash
-python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIPN' input_ligand='"CC(=O)NC1C(O)OC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3NC(C)=O)C(O)C2NC(C)=O)C1O"' input_template=data/test_cases/predicted_structures/T1152.pdb sample_id='T1152' out_path='./T1152_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
+python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling input_receptor='MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIP|MYTVKPGDTMWKIAVKYQIGISEIIAANPQIKNPNLIYPGQKINIPN' input_ligand='"CC(=O)NC1C(O)OC(CO)C(OC2OC(CO)C(OC3OC(CO)C(O)C(O)C3NC(C)=O)C(O)C2NC(C)=O)C1O"' input_template=data/test_cases/predicted_structures/T1152.pdb sample_id='T1152' out_path='./T1152_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=true auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
 ```
 
 If you do not already have a template protein structure available for your target of interest, set `input_template=null` to instead have the sampling script predict the ESMFold structure of your provided `input_protein` sequence before running the sampling pipeline. For more information regarding the input arguments available for sampling, please refer to the config at `configs/sample.yaml`.
@@ -369,7 +380,7 @@ If you do not already have a template protein structure available for your targe
 For instance, one can perform batched prediction as follows:
 
 ```bash
-python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights_EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling csv_path='./data/test_cases/prediction_inputs/flowdock_batched_inputs.csv' out_path='./T1152_batch_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=false auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
+python flowdock/sample.py ckpt_path=checkpoints/esmfold_prior_paper_weights-EMA.ckpt model.cfg.prior_type=esmfold sampling_task=batched_structure_sampling csv_path='./data/test_cases/prediction_inputs/flowdock_batched_inputs.csv' out_path='./T1152_batch_sampled_structures/' n_samples=5 chunk_size=5 num_steps=40 sampler=VDODE sampler_eta=1.0 start_time='1.0' use_template=true separate_pdb=true visualize_sample_trajectories=false auxiliary_estimation_only=false esmfold_chunk_size=null trainer=gpu
 ```
 
 </details>
diff --git a/configs/data/plinder.yaml b/configs/data/plinder.yaml
new file mode 100644
index 0000000..658a89b
--- /dev/null
+++ b/configs/data/plinder.yaml
@@ -0,0 +1,18 @@
+_target_: flowdock.data.plinder_datamodule.PlinderDataModule
+data_dir: ${paths.data_dir}/PLINDER/
+batch_size: 16 # Needs to be divisible by the number of devices (e.g., if in a distributed setup)
+num_workers: 4
+pin_memory: True
+# overfitting arguments
+overfitting_example_name: null # NOTE: currently not used
+# model arguments
+n_protein_patches: 96
+n_lig_patches: 32
+epoch_frac: 1.0
+edge_crop_size: 400000
+esm_version: ${model.cfg.protein_encoder.esm_version}
+esm_repr_layer: ${model.cfg.protein_encoder.esm_repr_layer}
+# general dataset arguments
+plinder_offline: False
+min_protein_length: 50
+max_protein_length: 750
diff --git a/environments/flowdock_environment.yaml b/environments/flowdock_environment.yaml
index a74a7f8..ab55337 100644
--- a/environments/flowdock_environment.yaml
+++ b/environments/flowdock_environment.yaml
@@ -1,448 +1,540 @@
 name: FlowDock
 channels:
-  - pyg
-  - iopath
   - pytorch
+  - pyg
   - senyan.dev
   - nvidia
-  - defaults
   - conda-forge
 dependencies:
   - _libgcc_mutex=0.1=conda_forge
-  - _openmp_mutex=4.5=2_kmp_llvm
-  - aiohttp=3.9.3=py39hd1e30aa_1
-  - aiosignal=1.3.1=pyhd8ed1ab_0
-  - antlr-python-runtime=4.9.3=pyhd8ed1ab_1
-  - anyio=4.2.0=py39h06a4308_0
-  - argon2-cffi=23.1.0=pyhd8ed1ab_0
-  - argon2-cffi-bindings=21.2.0=py39hd1e30aa_4
-  - arpack=3.7.0=hdefa2d7_2
-  - arrow=1.2.3=py39h06a4308_1
-  - astunparse=1.6.3=pyhd8ed1ab_0
-  - async-lru=2.0.4=pyhd8ed1ab_0
-  - async-timeout=4.0.3=pyhd8ed1ab_0
-  - attrs=22.1.0=py39h06a4308_0
-  - babel=2.14.0=pyhd8ed1ab_0
-  - backcall=0.2.0=pyhd3eb1b0_0
-  - backoff=2.2.1=pyhd8ed1ab_0
-  - beautifulsoup4=4.12.2=py39h06a4308_0
+  - _openmp_mutex=4.5=3_kmp_llvm
+  - aiohappyeyeballs=2.6.1=pyhd8ed1ab_0
+  - aiohttp=3.11.13=py310h89163eb_0
+  - aiosignal=1.3.2=pyhd8ed1ab_0
+  - alsa-lib=1.2.13=hb9d3cd8_0
+  - ambertools=24.8=cuda_None_nompi_py310h834fefc_101
+  - annotated-types=0.7.0=pyhd8ed1ab_1
+  - anyio=4.8.0=pyhd8ed1ab_0
+  - aom=3.9.1=hac33072_0
+  - argon2-cffi=23.1.0=pyhd8ed1ab_1
+  - argon2-cffi-bindings=21.2.0=py310ha75aee5_5
+  - arpack=3.9.1=nompi_hf03ea27_102
+  - arrow=1.3.0=pyhd8ed1ab_1
+  - asttokens=3.0.0=pyhd8ed1ab_1
+  - async-lru=2.0.4=pyhd8ed1ab_1
+  - async-timeout=5.0.1=pyhd8ed1ab_1
+  - attr=2.5.1=h166bdaf_1
+  - attrs=25.3.0=pyh71513ae_0
+  - babel=2.17.0=pyhd8ed1ab_0
+  - beautifulsoup4=4.13.3=pyha770c72_0
   - blas=2.116=mkl
   - blas-devel=3.9.0=16_linux64_mkl
-  - bleach=6.1.0=pyhd8ed1ab_0
-  - blessed=1.19.1=pyhe4f9e05_2
-  - blosc=1.21.5=h0f2a231_0
-  - boost=1.84.0=h8003fee_2
-  - boost-cpp=1.84.0=h44aadfe_2
-  - bottleneck=1.3.7=py39ha9d4c09_0
-  - brotli=1.0.9=h166bdaf_9
-  - brotli-bin=1.0.9=h166bdaf_9
-  - brotli-python=1.0.9=py39h5a03fae_9
-  - bson=0.5.9=py_0
-  - bzip2=1.0.8=h7f98852_4
-  - c-ares=1.25.0=hd590300_0
-  - c-blosc2=2.12.0=hb4ffafa_0
-  - ca-certificates=2024.2.2=hbcca054_0
-  - cachecontrol=0.13.1=pyhd8ed1ab_0
-  - cachecontrol-with-filecache=0.13.1=pyhd8ed1ab_0
+  - bleach=6.2.0=pyh29332c3_4
+  - bleach-with-css=6.2.0=h82add2a_4
+  - blosc=1.21.6=he440d0b_1
+  - brotli=1.1.0=hb9d3cd8_2
+  - brotli-bin=1.1.0=hb9d3cd8_2
+  - brotli-python=1.1.0=py310hf71b8c6_2
+  - bson=0.5.10=pyhd8ed1ab_0
+  - bzip2=1.0.8=h4bc722e_7
+  - c-ares=1.34.4=hb9d3cd8_0
+  - c-blosc2=2.15.2=h3122c55_1
+  - ca-certificates=2025.1.31=hbcca054_0
   - cached-property=1.5.2=hd8ed1ab_1
   - cached_property=1.5.2=pyha770c72_1
-  - cachetools=5.3.2=pyhd8ed1ab_0
-  - cairo=1.18.0=h3faef2a_0
-  - certifi=2024.2.2=pyhd8ed1ab_0
-  - cffi=1.15.1=py39h5eee18b_3
-  - cfgv=3.3.1=py39h06a4308_0
-  - charset-normalizer=3.2.0=pyhd8ed1ab_0
-  - cleo=2.0.1=py39h06a4308_0
-  - click=8.0.4=py39h06a4308_0
-  - colorama=0.4.6=pyhd8ed1ab_0
-  - comm=0.2.1=pyhd8ed1ab_0
-  - contourpy=1.2.0=py39hdb19cb5_0
-  - crashtest=0.4.1=py39h06a4308_0
-  - croniter=1.3.7=py39h06a4308_0
-  - cryptography=41.0.2=py39h22a60cf_0
+  - cachetools=5.5.2=pyhd8ed1ab_0
+  - cairo=1.18.4=h3394656_0
+  - certifi=2025.1.31=pyhd8ed1ab_0
+  - cffi=1.17.1=py310h8deb56e_0
+  - chardet=5.2.0=pyhd8ed1ab_3
+  - charset-normalizer=3.4.1=pyhd8ed1ab_0
+  - colorama=0.4.6=pyhd8ed1ab_1
+  - comm=0.2.2=pyhd8ed1ab_1
+  - contourpy=1.3.1=py310h3788b33_0
+  - cpython=3.10.16=py310hd8ed1ab_1
   - cuda-cudart=11.8.89=0
   - cuda-cupti=11.8.87=0
   - cuda-libraries=11.8.0=0
   - cuda-nvrtc=11.8.89=0
   - cuda-nvtx=11.8.86=0
   - cuda-runtime=11.8.0=0
-  - cudatoolkit=11.8.0=h4ba93d1_12
+  - cuda-version=11.8=h70ddcb2_3
+  - cudatoolkit=11.8.0=h4ba93d1_13
   - cudatoolkit-dev=11.8.0=h1fa729e_6
-  - curl=8.1.2=h409715c_0
-  - cycler=0.11.0=pyhd3eb1b0_0
-  - cython=3.0.7=py39h3d6467e_0
-  - dateutils=0.6.12=py_0
-  - dbus=1.13.18=hb2f20db_0
-  - debugpy=1.8.0=py39h3d6467e_1
-  - decorator=5.1.1=pyhd8ed1ab_0
-  - deepdiff=6.3.1=pyhd8ed1ab_0
+  - cycler=0.12.1=pyhd8ed1ab_1
+  - cyrus-sasl=2.1.27=h54b06d7_7
+  - dav1d=1.2.1=hd590300_0
+  - dbus=1.13.6=h5008d03_3
+  - debugpy=1.8.13=py310hf71b8c6_0
+  - decorator=5.2.1=pyhd8ed1ab_0
   - defusedxml=0.7.1=pyhd8ed1ab_0
-  - distlib=0.3.7=pyhd8ed1ab_0
-  - dulwich=0.21.3=py39h5eee18b_0
-  - entrypoints=0.4=pyhd8ed1ab_0
-  - exceptiongroup=1.0.4=py39h06a4308_0
-  - expat=2.6.2=h59595ed_0
-  - ffmpeg=4.3=hf484d3e_0
-  - fftw=3.3.10=nompi_hc118613_108
-  - filelock=3.12.2=pyhd8ed1ab_0
+  - deprecated=1.2.18=pyhd8ed1ab_0
+  - exceptiongroup=1.2.2=pyhd8ed1ab_1
+  - expat=2.6.4=h5888daf_0
+  - ffmpeg=7.1.1=gpl_h24e5c1d_701
+  - fftw=3.3.10=nompi_hf1063bd_110
+  - filelock=3.18.0=pyhd8ed1ab_0
+  - flexcache=0.3=pyhd8ed1ab_1
+  - flexparser=0.4=pyhd8ed1ab_1
   - font-ttf-dejavu-sans-mono=2.37=hab24e00_0
   - font-ttf-inconsolata=3.000=h77eed37_0
   - font-ttf-source-code-pro=2.038=h77eed37_0
-  - font-ttf-ubuntu=0.83=h77eed37_1
-  - fontconfig=2.14.2=h14ed4e7_0
+  - font-ttf-ubuntu=0.83=h77eed37_3
+  - fontconfig=2.15.0=h7e30c49_1
   - fonts-conda-ecosystem=1=0
   - fonts-conda-forge=1=0
-  - fonttools=4.25.0=pyhd3eb1b0_0
-  - fqdn=1.5.1=pyhd8ed1ab_0
-  - freetype=2.12.1=hca18f0e_1
-  - frozenlist=1.4.1=py39hd1e30aa_0
-  - fvcore=0.1.5.post20221221=pyhd8ed1ab_0
-  - glib=2.80.0=hf2295e7_1
-  - glib-tools=2.80.0=hde27a5a_1
-  - gmp=6.2.1=h58526e2_0
-  - gmpy2=2.1.2=py39h376b7d2_1
-  - gnutls=3.6.13=h85f3911_1
-  - greenlet=3.0.1=py39h6a678d5_0
-  - h11=0.12.0=pyhd3eb1b0_0
-  - hdf4=4.2.15=h9772cbc_5
-  - hdf5=1.14.1=nompi_h4f84152_100
-  - hydra-core=1.3.2=pyhd8ed1ab_0
-  - icu=73.2=h59595ed_0
-  - identify=2.5.5=py39h06a4308_0
-  - idna=3.4=pyhd8ed1ab_0
-  - importlib-metadata=6.0.0=py39h06a4308_0
-  - importlib-resources=3.3.1=pyhd8ed1ab_0
-  - importlib_metadata=6.0.0=hd3eb1b0_0
-  - importlib_resources=5.2.0=pyhd3eb1b0_1
-  - iniconfig=1.1.1=pyhd3eb1b0_0
-  - inquirer=3.1.3=pyhd8ed1ab_0
-  - iopath=0.1.9=py39
-  - ipykernel=6.28.0=py39h06a4308_0
-  - ipython=8.15.0=py39h06a4308_0
-  - ipywidgets=8.1.1=pyhd8ed1ab_0
-  - isoduration=20.11.0=pyhd8ed1ab_0
-  - itsdangerous=2.0.1=pyhd3eb1b0_0
-  - jaraco.classes=3.2.1=pyhd3eb1b0_0
-  - jedi=0.18.1=py39h06a4308_1
-  - jeepney=0.7.1=pyhd3eb1b0_0
-  - jinja2=3.1.2=pyhd8ed1ab_1
-  - joblib=1.3.2=pyhd8ed1ab_0
-  - jpeg=9e=h166bdaf_2
-  - json5=0.9.14=pyhd8ed1ab_0
-  - jsonpointer=2.4=py39hf3d152e_3
-  - jsonschema=4.17.3=py39h06a4308_0
-  - jsonschema-with-format-nongpl=4.17.3=pyhd8ed1ab_0
-  - jupyter-lsp=2.2.1=pyhd8ed1ab_0
-  - jupyter_client=8.6.0=pyhd8ed1ab_0
-  - jupyter_core=5.7.1=py39hf3d152e_0
-  - jupyter_events=0.6.3=pyhd8ed1ab_1
-  - jupyter_server=2.10.0=pyhd8ed1ab_0
-  - jupyter_server_terminals=0.5.1=pyhd8ed1ab_0
-  - jupyterlab=4.0.10=pyhd8ed1ab_0
-  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_0
-  - jupyterlab_server=2.24.0=pyhd8ed1ab_0
-  - jupyterlab_widgets=3.0.9=pyhd8ed1ab_0
-  - keyring=24.2.0=py39hf3d152e_0
+  - fonttools=4.56.0=py310h89163eb_0
+  - fqdn=1.5.1=pyhd8ed1ab_1
+  - freetype=2.13.3=h48d6fc4_0
+  - freetype-py=2.3.0=pyhd8ed1ab_0
+  - fribidi=1.0.10=h36c2ea0_0
+  - frozenlist=1.5.0=py310h89163eb_1
+  - fsspec=2025.3.0=pyhd8ed1ab_0
+  - gdk-pixbuf=2.42.12=hb9ae30d_0
+  - gettext=0.23.1=h5888daf_0
+  - gettext-tools=0.23.1=h5888daf_0
+  - gmp=6.3.0=hac33072_2
+  - gmpy2=2.1.5=py310he8512ff_3
+  - graphite2=1.3.13=h59595ed_1003
+  - greenlet=3.1.1=py310hf71b8c6_1
+  - h11=0.14.0=pyhd8ed1ab_1
+  - h2=4.2.0=pyhd8ed1ab_0
+  - harfbuzz=10.4.0=h76408a6_0
+  - hdf4=4.2.15=h2a13503_7
+  - hdf5=1.14.4=nompi_h2d575fe_105
+  - hpack=4.1.0=pyhd8ed1ab_0
+  - httpcore=1.0.7=pyh29332c3_1
+  - httpx=0.28.1=pyhd8ed1ab_0
+  - hyperframe=6.1.0=pyhd8ed1ab_0
+  - icu=75.1=he02047a_0
+  - idna=3.10=pyhd8ed1ab_1
+  - importlib-metadata=8.6.1=pyha770c72_0
+  - importlib_resources=6.5.2=pyhd8ed1ab_0
+  - ipykernel=6.29.5=pyh3099207_0
+  - ipython=8.34.0=pyh907856f_0
+  - isoduration=20.11.0=pyhd8ed1ab_1
+  - jack=1.9.22=h7c63dc7_2
+  - jedi=0.19.2=pyhd8ed1ab_1
+  - jinja2=3.1.6=pyhd8ed1ab_0
+  - joblib=1.4.2=pyhd8ed1ab_1
+  - json5=0.10.0=pyhd8ed1ab_1
+  - jsonpointer=3.0.0=py310hff52083_1
+  - jsonschema=4.23.0=pyhd8ed1ab_1
+  - jsonschema-specifications=2024.10.1=pyhd8ed1ab_1
+  - jsonschema-with-format-nongpl=4.23.0=hd8ed1ab_1
+  - jupyter-lsp=2.2.5=pyhd8ed1ab_1
+  - jupyter_client=8.6.3=pyhd8ed1ab_1
+  - jupyter_core=5.7.2=pyh31011fe_1
+  - jupyter_events=0.12.0=pyh29332c3_0
+  - jupyter_server=2.15.0=pyhd8ed1ab_0
+  - jupyter_server_terminals=0.5.3=pyhd8ed1ab_1
+  - jupyterlab=4.3.6=pyhd8ed1ab_0
+  - jupyterlab_pygments=0.3.0=pyhd8ed1ab_2
+  - jupyterlab_server=2.27.3=pyhd8ed1ab_1
+  - jupyterlab_widgets=3.0.13=pyhd8ed1ab_1
+  - kernel-headers_linux-64=3.10.0=he073ed8_18
   - keyutils=1.6.1=h166bdaf_0
-  - kiwisolver=1.4.4=py39h6a678d5_0
-  - krb5=1.20.1=h81ceb04_0
+  - kiwisolver=1.4.7=py310h3788b33_0
+  - krb5=1.21.3=h659f571_0
   - lame=3.100=h166bdaf_1003
-  - lcms2=2.12=hddcbb42_0
-  - ld_impl_linux-64=2.40=h41732ed_0
+  - lcms2=2.17=h717163a_0
+  - ld_impl_linux-64=2.43=h712a8e2_4
   - lerc=4.0.0=h27087fc_0
-  - libaec=1.1.2=h59595ed_1
+  - level-zero=1.21.2=h84d6215_0
+  - libabseil=20250127.0=cxx17_hbbce691_0
+  - libaec=1.1.3=h59595ed_0
+  - libasprintf=0.23.1=h8e693c7_0
+  - libasprintf-devel=0.23.1=h8e693c7_0
+  - libass=0.17.3=hba53ac1_1
   - libblas=3.9.0=16_linux64_mkl
-  - libboost=1.84.0=h8013b2b_2
-  - libboost-devel=1.84.0=h00ab1b0_2
-  - libboost-headers=1.84.0=ha770c72_2
-  - libboost-python=1.84.0=py39hda80f44_2
-  - libboost-python-devel=1.84.0=py39h8003fee_2
-  - libbrotlicommon=1.0.9=h166bdaf_9
-  - libbrotlidec=1.0.9=h166bdaf_9
-  - libbrotlienc=1.0.9=h166bdaf_9
+  - libboost=1.86.0=h6c02f8c_3
+  - libboost-python=1.86.0=py310ha2bacc8_3
+  - libbrotlicommon=1.1.0=hb9d3cd8_2
+  - libbrotlidec=1.1.0=hb9d3cd8_2
+  - libbrotlienc=1.1.0=hb9d3cd8_2
+  - libcap=2.75=h39aace5_0
   - libcblas=3.9.0=16_linux64_mkl
   - libcublas=11.11.3.6=0
   - libcufft=10.9.0.58=0
-  - libcufile=1.7.1.12=0
-  - libcurand=10.3.3.129=0
-  - libcurl=8.1.2=h409715c_0
+  - libcufile=1.9.1.3=0
+  - libcurand=10.3.5.147=0
+  - libcurl=8.12.1=h332b0f4_0
   - libcusolver=11.4.1.48=0
   - libcusparse=11.7.5.86=0
-  - libdeflate=1.17=h0b41bf4_0
-  - libedit=3.1.20191231=he28a2e2_2
+  - libdb=6.2.32=h9c3ff4c_0
+  - libdeflate=1.23=h4ddbbb0_0
+  - libdrm=2.4.124=hb9d3cd8_0
+  - libedit=3.1.20250104=pl5321h7949ede_0
+  - libegl=1.7.0=ha4b6fd6_2
   - libev=4.33=hd590300_2
-  - libexpat=2.6.2=h59595ed_0
-  - libffi=3.4.2=h7f98852_5
-  - libgcc-ng=13.1.0=he5830b7_0
-  - libgfortran-ng=13.1.0=h69a702a_0
-  - libgfortran5=13.1.0=h15d22d2_0
-  - libglib=2.80.0=hf2295e7_1
-  - libgomp=13.1.0=he5830b7_0
-  - libhwloc=2.4.1=h6746aa3_0
-  - libiconv=1.17=h166bdaf_0
-  - libjpeg-turbo=2.0.0=h9bf148f_0
+  - libexpat=2.6.4=h5888daf_0
+  - libffi=3.4.6=h2dba641_0
+  - libflac=1.4.3=h59595ed_0
+  - libgcc=14.2.0=h767d61c_2
+  - libgcc-ng=14.2.0=h69a702a_2
+  - libgcrypt-lib=1.11.0=hb9d3cd8_2
+  - libgettextpo=0.23.1=h5888daf_0
+  - libgettextpo-devel=0.23.1=h5888daf_0
+  - libgfortran=14.2.0=h69a702a_2
+  - libgfortran-ng=14.2.0=h69a702a_2
+  - libgfortran5=14.2.0=hf1ad2bd_2
+  - libgl=1.7.0=ha4b6fd6_2
+  - libglib=2.82.2=h2ff4ddf_1
+  - libglvnd=1.7.0=ha4b6fd6_2
+  - libglx=1.7.0=ha4b6fd6_2
+  - libgomp=14.2.0=h767d61c_2
+  - libgpg-error=1.51=hbd13f7d_1
+  - libhwloc=2.11.2=default_h0d58e46_1001
+  - libiconv=1.18=h4ce23a2_1
+  - libjpeg-turbo=3.0.0=hd590300_1
   - liblapack=3.9.0=16_linux64_mkl
   - liblapacke=3.9.0=16_linux64_mkl
-  - libnghttp2=1.58.0=h47da74e_1
+  - liblzma=5.6.4=hb9d3cd8_0
+  - libnetcdf=4.9.2=nompi_h5ddbaa4_116
+  - libnghttp2=1.64.0=h161d5f1_0
   - libnpp=11.8.0.86=0
-  - libnsl=2.0.0=h7f98852_0
+  - libnsl=2.0.1=hd590300_0
+  - libntlm=1.8=hb9d3cd8_0
   - libnvjpeg=11.9.0.86=0
-  - libpng=1.6.39=h753d276_0
-  - libsodium=1.0.18=h36c2ea0_1
-  - libsqlite=3.42.0=h2797004_0
-  - libssh2=1.11.0=h0841786_0
-  - libstdcxx-ng=13.1.0=hfd8a6a1_0
-  - libtiff=4.2.0=hf544144_3
+  - libogg=1.3.5=h4ab18f5_0
+  - libopenvino=2025.0.0=hdc3f47d_3
+  - libopenvino-auto-batch-plugin=2025.0.0=h4d9b6c2_3
+  - libopenvino-auto-plugin=2025.0.0=h4d9b6c2_3
+  - libopenvino-hetero-plugin=2025.0.0=h981d57b_3
+  - libopenvino-intel-cpu-plugin=2025.0.0=hdc3f47d_3
+  - libopenvino-intel-gpu-plugin=2025.0.0=hdc3f47d_3
+  - libopenvino-intel-npu-plugin=2025.0.0=hdc3f47d_3
+  - libopenvino-ir-frontend=2025.0.0=h981d57b_3
+  - libopenvino-onnx-frontend=2025.0.0=h0e684df_3
+  - libopenvino-paddle-frontend=2025.0.0=h0e684df_3
+  - libopenvino-pytorch-frontend=2025.0.0=h5888daf_3
+  - libopenvino-tensorflow-frontend=2025.0.0=h684f15b_3
+  - libopenvino-tensorflow-lite-frontend=2025.0.0=h5888daf_3
+  - libopus=1.3.1=h7f98852_1
+  - libpciaccess=0.18=hd590300_0
+  - libpng=1.6.47=h943b412_0
+  - libpq=17.4=h27ae623_0
+  - libprotobuf=5.29.3=h501fc15_0
+  - librdkit=2024.09.6=h84b0b3c_0
+  - librsvg=2.58.4=h49af25d_2
+  - libsndfile=1.2.2=hc60ed4a_1
+  - libsodium=1.0.20=h4ab18f5_0
+  - libsqlite=3.49.1=hee588c1_1
+  - libssh2=1.11.1=hf672d98_0
+  - libstdcxx=14.2.0=h8f9b012_2
+  - libstdcxx-ng=14.2.0=h4852527_2
+  - libsystemd0=257.4=h4e0b6ca_1
+  - libtiff=4.7.0=hd9ff511_3
+  - libudev1=257.4=hbe16f8c_1
+  - libunwind=1.6.2=h9c3ff4c_0
+  - liburing=2.9=h84d6215_0
+  - libusb=1.0.27=hb9d3cd8_101
   - libuuid=2.38.1=h0b41bf4_0
-  - libwebp-base=1.3.1=hd590300_0
-  - libxcb=1.15=h0b41bf4_0
+  - libva=2.22.0=h4f16b4b_2
+  - libvorbis=1.3.7=h9c3ff4c_0
+  - libvpx=1.14.1=hac33072_0
+  - libwebp-base=1.5.0=h851e524_0
+  - libxcb=1.17.0=h8a09558_0
   - libxcrypt=4.4.36=hd590300_1
-  - libxml2=2.12.6=h232c23b_1
+  - libxkbcommon=1.8.1=hc4a0caf_0
+  - libxml2=2.13.6=h8d12d68_0
   - libxslt=1.1.39=h76b75d6_0
-  - libzip=1.10.1=h2629f0a_3
-  - libzlib=1.2.13=hd590300_5
+  - libzip=1.11.2=h6991a6a_0
+  - libzlib=1.3.1=hb9d3cd8_2
   - llvm-openmp=15.0.7=h0cdce71_0
-  - lxml=4.8.0=py39hb9d737c_3
-  - lz4-c=1.9.3=h9c3ff4c_1
-  - lzo=2.10=h516909a_1000
-  - markdown-it-py=2.2.0=py39h06a4308_1
-  - markupsafe=2.1.1=py39h7f8727e_0
-  - matplotlib-base=3.8.0=py39he9076e7_1
-  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
-  - mda-xdrlib=0.2.0=pyhd8ed1ab_0
-  - mdtraj=1.9.9=py39h67e0f1a_1
-  - mdurl=0.1.0=py39h06a4308_0
-  - mendeleev=0.15.0=pyhc1e730c_0
-  - mistune=3.0.2=pyhd8ed1ab_0
+  - lxml=5.3.1=py310h6ee67d5_0
+  - lz4-c=1.10.0=h5888daf_1
+  - markupsafe=3.0.2=py310h89163eb_1
+  - matplotlib-base=3.10.1=py310h68603db_0
+  - matplotlib-inline=0.1.7=pyhd8ed1ab_1
+  - mda-xdrlib=0.2.0=pyhd8ed1ab_1
+  - mdtraj=1.10.3=py310h4cdbd58_0
+  - mendeleev=0.20.1=pymin39_ha308f57_3
+  - mistune=3.1.2=pyhd8ed1ab_0
   - mkl=2022.1.0=h84fe81f_915
   - mkl-devel=2022.1.0=ha770c72_916
   - mkl-include=2022.1.0=h84fe81f_915
-  - more-itertools=8.12.0=pyhd3eb1b0_0
-  - mpc=1.3.1=hfe3b2da_0
-  - mpfr=4.2.0=hb012696_0
-  - mpmath=1.3.0=py39h06a4308_0
-  - msgpack-python=1.0.3=py39hd09550d_0
-  - multidict=6.0.5=py39hd1e30aa_0
+  - mpc=1.3.1=h24ddda3_1
+  - mpfr=4.2.1=h90cbb55_3
+  - mpg123=1.32.9=hc50e24c_0
+  - mpmath=1.3.0=pyhd8ed1ab_1
+  - multidict=6.1.0=py310h89163eb_2
   - munkres=1.1.4=pyh9f0ad1d_0
-  - nbclient=0.8.0=pyhd8ed1ab_0
-  - nbconvert-core=7.14.1=pyhd8ed1ab_0
-  - nbformat=5.9.2=pyhd8ed1ab_0
-  - ncurses=6.4=hcb278e6_0
-  - nest-asyncio=1.5.8=pyhd8ed1ab_0
-  - nettle=3.6=he412f7d_0
-  - networkx=3.1=py39h06a4308_0
-  - nodeenv=1.7.0=py39h06a4308_0
-  - notebook=7.0.6=pyhd8ed1ab_0
-  - notebook-shim=0.2.3=pyhd8ed1ab_0
-  - numexpr=2.8.0=mkl_py39h25e7801_2
-  - ocl-icd=2.3.1=h7f98852_0
+  - nbclient=0.10.2=pyhd8ed1ab_0
+  - nbconvert-core=7.16.6=pyh29332c3_0
+  - nbformat=5.10.4=pyhd8ed1ab_1
+  - ncurses=6.5=h2d0b736_3
+  - nest-asyncio=1.6.0=pyhd8ed1ab_1
+  - netcdf-fortran=4.6.1=nompi_ha5d1325_108
+  - networkx=3.4.2=pyh267e887_2
+  - notebook=7.3.3=pyhd8ed1ab_0
+  - notebook-shim=0.2.4=pyhd8ed1ab_1
+  - numexpr=2.7.3=py310hb5077e9_1
+  - ocl-icd=2.3.2=hb9d3cd8_2
   - ocl-icd-system=1.0.0=1
-  - olefile=0.47=pyhd8ed1ab_0
-  - omegaconf=2.3.0=pyhd8ed1ab_0
+  - opencl-headers=2024.10.24=h5888daf_0
   - openff-amber-ff-ports=0.0.4=pyhca7485f_0
-  - openff-forcefields=2023.11.0=pyhca7485f_0
-  - openff-interchange-base=0.3.18=pyhd8ed1ab_0
-  - openff-models=0.1.1=pyhca7485f_0
-  - openff-toolkit-base=0.14.5=pyhd8ed1ab_1
-  - openff-units=0.2.1=pyh1a96a4e_0
-  - openff-utilities=0.1.12=pyhd8ed1ab_0
-  - openh264=2.1.1=h780b84a_0
-  - openjpeg=2.4.0=hb52868f_1
-  - openmm=8.1.0=py39he81762f_1
-  - openssl=3.2.1=hd590300_1
-  - ordered-set=4.1.0=py39h06a4308_0
-  - orjson=3.8.8=py39h52d8a92_0
-  - overrides=7.4.0=pyhd8ed1ab_0
-  - packaging=23.1=py39h06a4308_0
-  - packmol=20.010=h86c2bf4_0
-  - pandas=2.1.4=py39h1128e8f_0
-  - pandocfilters=1.5.0=pyhd8ed1ab_0
-  - panedr=0.8.0=pyhd8ed1ab_0
-  - parmed=3.4.4=py39h227be39_0
-  - parso=0.8.3=pyhd8ed1ab_0
-  - pcre=8.45=h295c915_0
-  - pcre2=10.43=hcad00b1_0
-  - pdbfixer=1.9=pyh1a96a4e_0
+  - openff-forcefields=2024.09.0=pyhff2d567_0
+  - openff-interchange=0.4.2=pyhd8ed1ab_2
+  - openff-interchange-base=0.4.2=pyhd8ed1ab_2
+  - openff-toolkit=0.16.8=pyhd8ed1ab_2
+  - openff-toolkit-base=0.16.8=pyhd8ed1ab_2
+  - openff-units=0.3.0=pyhd8ed1ab_1
+  - openff-utilities=0.1.15=pyhd8ed1ab_0
+  - openh264=2.6.0=hc22cd8d_0
+  - openjpeg=2.5.3=h5fbd93e_0
+  - openldap=2.6.9=he970967_0
+  - openmm=8.2.0=py310h30bdd6a_2
+  - openmmforcefields=0.14.2=pyhd8ed1ab_0
+  - openssl=3.4.1=h7b32b05_0
+  - overrides=7.7.0=pyhd8ed1ab_1
+  - packaging=24.2=pyhd8ed1ab_2
+  - panedr=0.8.0=pyhd8ed1ab_1
+  - pango=1.56.2=h861ebed_0
+  - parmed=4.3.0=py310h78e4988_1
+  - parso=0.8.4=pyhd8ed1ab_1
+  - pcre2=10.44=hba22ea6_2
+  - pdbfixer=1.11=pyhd8ed1ab_0
   - perl=5.32.1=7_hd590300_perl5
-  - pexpect=4.8.0=pyhd3eb1b0_3
-  - pickleshare=0.7.5=py_1003
-  - pillow=8.2.0=py39hf95b381_1
-  - pint=0.21=pyhd8ed1ab_0
-  - pip=23.2.1=pyhd8ed1ab_0
-  - pixman=0.43.2=h59595ed_0
-  - pkginfo=1.9.6=py39h06a4308_0
-  - platformdirs=3.10.0=py39h06a4308_0
-  - pluggy=1.0.0=py39h06a4308_1
-  - poetry=1.6.1=linux_pyha804496_0
-  - poetry-core=1.7.0=pyhd8ed1ab_0
-  - poetry-plugin-export=1.5.0=pyhd8ed1ab_0
-  - portalocker=2.8.2=py39hf3d152e_1
-  - pre-commit=3.2.1=py39h06a4308_0
-  - prometheus_client=0.19.0=pyhd8ed1ab_0
-  - prompt-toolkit=3.0.43=py39h06a4308_0
-  - psutil=5.9.0=py39h5eee18b_0
-  - pthread-stubs=0.4=h36c2ea0_1001
-  - ptyprocess=0.7.0=pyhd3eb1b0_2
-  - pure_eval=0.2.2=pyhd8ed1ab_0
-  - py-cpuinfo=9.0.0=pyhd8ed1ab_0
-  - pycairo=1.25.0=py39hc92de75_1
-  - pycparser=2.21=pyhd3eb1b0_0
-  - pydantic=1.10.8=py39h5eee18b_0
-  - pyedr=0.8.0=pyhd8ed1ab_0
+  - pexpect=4.9.0=pyhd8ed1ab_1
+  - pickleshare=0.7.5=pyhd8ed1ab_1004
+  - pillow=11.1.0=py310h7e6dc6c_0
+  - pint=0.24.4=pyhd8ed1ab_1
+  - pip=25.0.1=pyh8b19718_0
+  - pixman=0.44.2=h29eaf8c_0
+  - pkgutil-resolve-name=1.3.10=pyhd8ed1ab_2
+  - platformdirs=4.3.6=pyhd8ed1ab_1
+  - prometheus_client=0.21.1=pyhd8ed1ab_0
+  - prompt-toolkit=3.0.50=pyha770c72_0
+  - psutil=7.0.0=py310ha75aee5_0
+  - pthread-stubs=0.4=hb9d3cd8_1002
+  - ptyprocess=0.7.0=pyhd8ed1ab_1
+  - pugixml=1.15=h3f63f65_0
+  - pulseaudio-client=17.0=hb77b528_0
+  - pure_eval=0.2.3=pyhd8ed1ab_1
+  - py-cpuinfo=9.0.0=pyhd8ed1ab_1
+  - pycairo=1.27.0=py310h25ff670_0
+  - pycparser=2.22=pyh29332c3_1
+  - pydantic=2.10.6=pyh3cfb1c2_0
+  - pydantic-core=2.27.2=py310h505e2c1_0
+  - pyedr=0.8.0=pyhd8ed1ab_1
   - pyfiglet=0.8.post1=py_0
-  - pyg=2.5.2=py39_torch_2.2.0_cu118
-  - pygments=2.15.1=py39h06a4308_1
-  - pyjwt=2.4.0=py39h06a4308_0
-  - pynvml=11.5.0=pyhd8ed1ab_0
-  - pyparsing=3.0.9=py39h06a4308_0
-  - pyproject_hooks=1.0.0=py39h06a4308_0
-  - pyrsistent=0.18.0=py39heee7806_0
-  - pysocks=1.7.1=pyha2e5f31_6
-  - pytables=3.8.0=py39hb8e3aad_2
-  - pytest=7.4.0=py39h06a4308_0
-  - python=3.9.17=h0755675_0_cpython
-  - python-build=0.10.0=py39h06a4308_0
-  - python-constraint=1.4.0=py_0
-  - python-dateutil=2.8.2=pyhd3eb1b0_0
-  - python-editor=1.0.4=pyhd3eb1b0_0
-  - python-fastjsonschema=2.19.1=pyhd8ed1ab_0
-  - python-installer=0.7.0=pyhd8ed1ab_0
-  - python-json-logger=2.0.7=pyhd8ed1ab_0
-  - python-multipart=0.0.6=pyhd8ed1ab_0
-  - python-tzdata=2023.4=pyhd8ed1ab_0
-  - python_abi=3.9=3_cp39
-  - pytorch=2.2.1=py3.9_cuda11.8_cudnn8.7.0_0
-  - pytorch-cuda=11.8=h7e8668a_5
+  - pyg=2.5.2=py310_torch_2.2.0_cu118
+  - pygments=2.19.1=pyhd8ed1ab_0
+  - pyparsing=3.2.1=pyhd8ed1ab_0
+  - pysocks=1.7.1=pyha55dd90_7
+  - pytables=3.10.1=py310h431dcdc_4
+  - python=3.10.16=he725a3c_1_cpython
+  - python-constraint=1.4.0=pyhff2d567_1
+  - python-dateutil=2.9.0.post0=pyhff2d567_1
+  - python-fastjsonschema=2.21.1=pyhd8ed1ab_0
+  - python-tzdata=2025.1=pyhd8ed1ab_0
+  - python_abi=3.10=5_cp310
+  - pytorch=2.2.1=py3.10_cuda11.8_cudnn8.7.0_0
+  - pytorch-cuda=11.8=h7e8668a_6
   - pytorch-mutex=1.0=cuda
-  - pytorch-scatter=2.1.2=py39_torch_2.2.0_cu118
-  - pytz=2023.3.post1=py39h06a4308_0
-  - pyyaml=6.0.1=py39hd1e30aa_0
-  - pyzmq=25.1.2=py39h6a678d5_0
-  - rapidfuzz=2.13.7=py39h417a72b_0
-  - rdkit=2024.03.1=py39h6cc1c65_0
-  - readchar=4.0.5=pyhd8ed1ab_0
-  - readline=8.2=h8228510_1
-  - reportlab=3.5.68=py39he59360d_1
-  - requests=2.31.0=pyhd8ed1ab_0
-  - requests-toolbelt=1.0.0=py39h06a4308_0
-  - rfc3339-validator=0.1.4=pyhd8ed1ab_0
+  - pytorch-scatter=2.1.2=py310_torch_2.2.0_cu118
+  - pytz=2025.1=pyhd8ed1ab_0
+  - pyyaml=6.0.2=py310h89163eb_2
+  - pyzmq=26.3.0=py310h71f11fc_0
+  - qhull=2020.2=h434a139_5
+  - rdkit=2024.09.6=py310hcd13295_0
+  - readline=8.2=h8c095d6_2
+  - referencing=0.36.2=pyh29332c3_0
+  - reportlab=4.3.1=py310ha75aee5_0
+  - requests=2.32.3=pyhd8ed1ab_1
+  - rfc3339-validator=0.1.4=pyhd8ed1ab_1
   - rfc3986-validator=0.1.1=pyh9f0ad1d_0
-  - rich=13.3.5=py39h06a4308_0
-  - rocm-smi=5.6.0=h59595ed_1
-  - scikit-learn=1.4.1.post1=py39ha22ef79_0
-  - scipy=1.12.0=py39h474f0d3_2
-  - secretstorage=3.3.1=py39h06a4308_1
-  - send2trash=1.8.2=pyh41d4057_0
-  - setuptools=63.4.1=py39h06a4308_0
-  - shellingham=1.5.0=py39h06a4308_0
-  - six=1.16.0=pyhd3eb1b0_1
+  - rlpycairo=0.2.0=pyhd8ed1ab_0
+  - rpds-py=0.23.1=py310hc1293b2_0
+  - scikit-learn=1.6.1=py310h27f47ee_0
+  - scipy=1.15.2=py310h1d65ade_0
+  - sdl2=2.32.50=h9b8e6db_1
+  - sdl3=3.2.8=h3083f51_0
+  - send2trash=1.8.3=pyh0d859eb_1
+  - setuptools=75.8.2=pyhff2d567_0
+  - six=1.17.0=pyhd8ed1ab_0
   - smirnoff99frosst=1.1.0=pyh44b312d_0
-  - snappy=1.1.10=h9fff704_0
-  - sniffio=1.2.0=py39h06a4308_1
-  - soupsieve=2.4=py39h06a4308_0
-  - sqlalchemy=2.0.25=py39h5eee18b_0
-  - stack_data=0.6.2=pyhd8ed1ab_0
-  - starlette=0.27.0=pyhd8ed1ab_0
-  - starsessions=1.3.0=pyhd8ed1ab_0
-  - sympy=1.12=pypyh9d50eac_103
-  - tabulate=0.9.0=pyhd8ed1ab_1
-  - tbb=2021.7.0=h924138e_0
-  - termcolor=2.4.0=pyhd8ed1ab_0
-  - terminado=0.18.0=pyh0d859eb_0
-  - threadpoolctl=3.4.0=pyhc1e730c_0
-  - tinycss2=1.2.1=pyhd8ed1ab_0
-  - tinydb=4.8.0=pyhd8ed1ab_0
-  - tk=8.6.12=h27826a3_0
-  - tomli=2.0.1=py39h06a4308_0
-  - tomlkit=0.12.1=pyha770c72_0
-  - torchaudio=2.2.1=py39_cu118
-  - torchtriton=2.2.0=py39
-  - torchvision=0.17.1=py39_cu118
-  - tornado=6.3.3=py39h5eee18b_0
-  - tqdm=4.66.1=pyhd8ed1ab_0
-  - traitlets=5.7.1=py39h06a4308_0
-  - trove-classifiers=2023.3.9=py39h06a4308_0
-  - typing-extensions=4.8.0=hd8ed1ab_0
-  - typing_extensions=4.8.0=pyha770c72_0
-  - typing_utils=0.1.0=pyhd8ed1ab_0
-  - tzdata=2023c=h71feb2d_0
-  - ukkonen=1.0.1=py39hdb19cb5_0
-  - unicodedata2=15.1.0=py39hd1e30aa_0
-  - uri-template=1.3.0=pyhd8ed1ab_0
-  - urllib3=2.1.0=py39h06a4308_1
-  - uvicorn=0.20.0=py39h06a4308_0
-  - validators=0.22.0=pyhd8ed1ab_0
-  - virtualenv=20.24.3=pyhd8ed1ab_0
-  - wcwidth=0.2.5=pyhd3eb1b0_0
-  - webcolors=1.13=pyhd8ed1ab_0
-  - webencodings=0.5.1=pyhd8ed1ab_2
-  - websocket-client=0.58.0=py39h06a4308_4
-  - websockets=10.4=py39h5eee18b_1
-  - wheel=0.41.1=pyhd8ed1ab_0
-  - widgetsnbextension=4.0.9=pyhd8ed1ab_0
-  - xmltodict=0.13.0=pyhd8ed1ab_0
-  - xorg-kbproto=1.0.7=h7f98852_1002
-  - xorg-libice=1.1.1=hd590300_0
-  - xorg-libsm=1.2.4=h7391055_0
-  - xorg-libx11=1.8.7=h8ee46fc_0
-  - xorg-libxau=1.0.11=hd590300_0
-  - xorg-libxdmcp=1.1.3=h7f98852_0
-  - xorg-libxext=1.3.4=h0b41bf4_2
-  - xorg-libxrender=0.9.11=hd590300_0
-  - xorg-renderproto=0.11.1=h7f98852_1002
-  - xorg-xextproto=7.3.0=h0b41bf4_1003
-  - xorg-xproto=7.0.31=h7f98852_1007
-  - xz=5.2.6=h166bdaf_0
-  - yacs=0.1.8=pyhd8ed1ab_0
-  - yaml=0.2.5=h7b6447c_0
-  - yarl=1.9.4=py39hd1e30aa_0
-  - zeromq=4.3.5=h59595ed_0
-  - zipp=3.11.0=py39h06a4308_0
-  - zlib=1.2.13=hd590300_5
-  - zlib-ng=2.0.7=h0b41bf4_0
-  - zstd=1.5.5=hfc55251_0
+  - snappy=1.2.1=h8bd8927_1
+  - sniffio=1.3.1=pyhd8ed1ab_1
+  - sqlalchemy=2.0.39=py310ha75aee5_1
+  - stack_data=0.6.3=pyhd8ed1ab_1
+  - svt-av1=3.0.1=h5888daf_0
+  - sympy=1.13.3=pyh2585a3b_105
+  - sysroot_linux-64=2.17=h0157908_18
+  - tbb=2021.13.0=hceb3a55_1
+  - terminado=0.18.1=pyh0d859eb_0
+  - threadpoolctl=3.6.0=pyhecae5ae_0
+  - tinycss2=1.4.0=pyhd8ed1ab_0
+  - tinydb=4.8.2=pyhd8ed1ab_1
+  - tk=8.6.13=noxft_h4845f30_101
+  - tomli=2.2.1=pyhd8ed1ab_1
+  - torchaudio=2.2.1=py310_cu118
+  - torchtriton=2.2.0=py310
+  - torchvision=0.17.1=py310_cu118
+  - tornado=6.4.2=py310ha75aee5_0
+  - tqdm=4.67.1=pyhd8ed1ab_1
+  - traitlets=5.14.3=pyhd8ed1ab_1
+  - types-python-dateutil=2.9.0.20241206=pyhd8ed1ab_0
+  - typing-extensions=4.12.2=hd8ed1ab_1
+  - typing_extensions=4.12.2=pyha770c72_1
+  - typing_utils=0.1.0=pyhd8ed1ab_1
+  - tzdata=2025a=h78e105d_0
+  - unicodedata2=16.0.0=py310ha75aee5_0
+  - uri-template=1.3.0=pyhd8ed1ab_1
+  - urllib3=2.3.0=pyhd8ed1ab_0
+  - validators=0.34.0=pyhd8ed1ab_1
+  - wayland=1.23.1=h3e06ad9_0
+  - wayland-protocols=1.41=hd8ed1ab_0
+  - wcwidth=0.2.13=pyhd8ed1ab_1
+  - webcolors=24.11.1=pyhd8ed1ab_0
+  - webencodings=0.5.1=pyhd8ed1ab_3
+  - websocket-client=1.8.0=pyhd8ed1ab_1
+  - wheel=0.45.1=pyhd8ed1ab_1
+  - wrapt=1.17.2=py310ha75aee5_0
+  - x264=1!164.3095=h166bdaf_2
+  - x265=3.5=h924138e_3
+  - xkeyboard-config=2.43=hb9d3cd8_0
+  - xmltodict=0.14.2=pyhd8ed1ab_1
+  - xorg-libice=1.1.2=hb9d3cd8_0
+  - xorg-libsm=1.2.6=he73a12e_0
+  - xorg-libx11=1.8.12=h4f16b4b_0
+  - xorg-libxau=1.0.12=hb9d3cd8_0
+  - xorg-libxcursor=1.2.3=hb9d3cd8_0
+  - xorg-libxdmcp=1.1.5=hb9d3cd8_0
+  - xorg-libxext=1.3.6=hb9d3cd8_0
+  - xorg-libxfixes=6.0.1=hb9d3cd8_0
+  - xorg-libxrender=0.9.12=hb9d3cd8_0
+  - xorg-libxscrnsaver=1.2.4=hb9d3cd8_0
+  - xorg-libxt=1.3.1=hb9d3cd8_0
+  - yaml=0.2.5=h7f98852_2
+  - yarl=1.18.3=py310h89163eb_1
+  - zeromq=4.3.5=h3b0a872_7
+  - zipp=3.21.0=pyhd8ed1ab_1
+  - zlib=1.3.1=hb9d3cd8_2
+  - zlib-ng=2.2.4=h7955e40_0
+  - zstandard=0.23.0=py310ha75aee5_1
+  - zstd=1.5.7=hb8e6e7a_1
   - pip:
       - absl-py==2.1.0
-      - appdirs==1.4.4
-      - asttokens==2.4.1
-      - beartype==0.17.2
-      - biopandas==0.4.1
-      - biopython>=1.79
-      - colorlog==6.8.2
-      - contextlib2==21.6.0
-      - deprecated==1.2.14
+      - alembic==1.15.1
+      - amberutils==21.0
+      - antlr4-python3-runtime==4.9.3
+      - autopage==0.5.2
+      - beartype==0.20.0
+      - biopandas==0.5.1
+      - biopython==1.79
+      - biotite==1.1.0
+      - biotraj==1.2.2
+      - cfgv==3.4.0
+      - cftime==1.6.4.post1
+      - click==8.1.8
+      - cliff==4.9.1
+      - cloudpathlib==0.21.0
+      - cmaes==0.11.1
+      - cmd2==2.5.11
+      - colorlog==6.9.0
+      - distlib==0.3.9
       - git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769
-      - dm-tree==0.1.8
+      - dm-tree==0.1.9
       - docker-pycreds==0.4.0
-      - einops==0.7.0
-      - executing==2.0.1
+      - duckdb==1.2.1
+      - edgembar==3.0
+      - einops==0.8.1
+      - eval-type-backport==0.2.2
+      - executing==2.2.0
       - fair-esm==2.0.0
       - fairscale==0.4.13
-      - fastcore==1.5.29
-      - fsspec==2024.2.0
-      - gitdb==4.0.11
-      - gitpython==3.1.42
+      - fastcore==1.7.29
+      - future==1.0.0
+      - fvcore==0.1.5.post20221221
+      - gcsfs==2025.3.0
+      - gemmi==0.7.0
+      - gitdb==4.0.12
+      - gitpython==3.1.44
+      - google-api-core==2.24.2
+      - google-auth==2.38.0
+      - google-auth-oauthlib==1.2.1
+      - google-cloud-core==2.4.3
+      - google-cloud-storage==3.1.0
+      - google-crc32c==1.6.0
+      - google-resumable-media==2.7.2
+      - googleapis-common-protos==1.69.1
       - hydra-colorlog==1.2.0
-      - lightning==2.2.2
-      - lightning-utilities==0.11.1
-      - lovely-numpy==0.2.11
-      - lovely-tensors==0.1.15
-      - ml-collections==0.1.1
+      - hydra-core==1.3.2
+      - hydra-optuna-sweeper==1.2.0
+      - identify==2.6.9
+      - iniconfig==2.0.0
+      - iopath==0.1.10
+      - ipython-genutils==0.2.0
+      - ipywidgets==7.8.5
+      - jupyterlab-widgets==1.1.11
+      - lightning==2.5.0.post0
+      - lightning-utilities==0.14.1
+      - looseversion==1.1.2
+      - lovely-numpy==0.2.13
+      - lovely-tensors==0.1.18
+      - mako==1.3.9
+      - markdown-it-py==3.0.0
+      - mdurl==0.1.2
+      - ml-collections==1.0.0
+      - mmcif==0.91.0
+      - mmpbsa-py==16.0
+      - mmtf-python==1.1.3
+      - mols2grid==2.0.0
+      - msgpack==1.1.0
       - msgpack-numpy==0.4.8
-      - numpy==1.23.5
+      - narwhals==1.30.0
+      - netcdf4==1.7.2
+      - nodeenv==1.9.1
+      - numpy==1.26.4
+      - oauthlib==3.2.2
+      - omegaconf==2.3.0
       - git+https://github.com/amorehead/openfold.git@fe1275099639bf7e617e09ef24d6af778647dd64
+      - optuna==2.10.1
+      - packmol-memgen==2025.1.29
+      - pandas==2.2.3
+      - pandocfilters==1.5.1
+      - pbr==6.1.1
+      - pdb4amber==22.0
+      - plinder==0.2.24
+      - plotly==6.0.0
+      - pluggy==1.5.0
+      - portalocker==3.1.1
       - posebusters==0.2.13
       - git+https://git@github.com/zrqiao/power_spherical.git@290b1630c5f84e3bb0d61711046edcf6e47200d4
-      - prody==2.4.1
-      - protobuf==4.25.3
+      - pre-commit==4.1.0
+      - prettytable==3.15.1
+      # - prody==2.4.1  # NOTE: we must `pip` install Prody to skip its NumPy dependency
+      - propcache==0.3.0
+      - proto-plus==1.26.1
+      - protobuf==5.29.3
+      - pyarrow==19.0.1
+      - pyasn1==0.6.1
+      - pyasn1-modules==0.4.1
+      - pymsmt==22.0
+      - pyperclip==1.9.0
+      - pytest==8.3.5
       - python-dotenv==1.0.1
-      - pytorch-lightning==2.2.2
+      - python-json-logger==3.3.0
+      - pytorch-lightning==2.5.0.post0
       - git+https://github.com/facebookresearch/pytorch3d.git@3da7703c5ac10039645966deddffe8db52eab8c5
+      - pytraj==2.0.6
+      - requests-oauthlib==2.0.0
+      - rich==13.9.4
       - rootutils==1.0.7
+      - rsa==4.9
+      - sander==22.0
       - seaborn==0.13.2
-      - sentry-sdk==1.40.5
-      - setproctitle==1.3.3
-      - smmap==5.0.1
-      - torchmetrics==1.3.1
-      - wandb==0.17.0
-      - wrapt==1.16.0
+      - sentry-sdk==2.22.0
+      - setproctitle==1.3.5
+      - smmap==5.0.2
+      - soupsieve==2.6
+      - stevedore==5.4.1
+      - tabulate==0.9.0
+      - termcolor==2.5.0
+      - torchmetrics==1.6.3
+      - virtualenv==20.29.3
+      - wandb==0.19.8
+      - widgetsnbextension==3.6.10
+      - yacs==0.1.8
diff --git a/environments/flowdock_environment_docker.yaml b/environments/flowdock_environment_docker.yaml
index 14b9d4f..10acdca 100644
--- a/environments/flowdock_environment_docker.yaml
+++ b/environments/flowdock_environment_docker.yaml
@@ -1,50 +1,58 @@
 name: flowdock
 channels:
   - pyg
-  - iopath
   - pytorch
   - nvidia
   - defaults
   - conda-forge
 dependencies:
-  - hydra-core=1.3.2=pyhd8ed1ab_0
-  - mendeleev=0.15.0=pyhc1e730c_0
-  - msgpack-python=1.0.3=py39hd09550d_0
-  - networkx=3.1=py39h06a4308_0
-  - omegaconf=2.3.0=pyhd8ed1ab_0
-  - pandas=2.1.4=py39h1128e8f_0
-  - python=3.9.17=h0755675_0_cpython
-  - pytorch=2.2.1=py3.9_cuda11.8_cudnn8.7.0_0
-  - pytorch-cuda=11.8=h7e8668a_5
+  - mendeleev=0.20.1=pymin39_ha308f57_3
+  - networkx=3.4.2=pyh267e887_2
+  - python=3.10.16=he725a3c_1_cpython
+  - pytorch=2.2.1=py3.10_cuda11.8_cudnn8.7.0_0
+  - pytorch-cuda=11.8=h7e8668a_6
   - pytorch-mutex=1.0=cuda
-  - pytorch-scatter=2.1.2=py39_torch_2.2.0_cu118
-  - rdkit=2024.03.1=py39h6cc1c65_0
-  - rich=13.3.5=py39h06a4308_0
-  - scikit-learn=1.4.1.post1=py39ha22ef79_0
-  - scipy=1.12.0=py39h474f0d3_2
-  - torchaudio=2.2.1=py39_cu118
-  - torchtriton=2.2.0=py39
-  - torchvision=0.17.1=py39_cu118
-  - tqdm=4.66.1=pyhd8ed1ab_0
+  - pytorch-scatter=2.1.2=py310_torch_2.2.0_cu118
+  - rdkit=2024.09.6=py310hcd13295_0
+  - scikit-learn=1.6.1=py310h27f47ee_0
+  - scipy=1.15.2=py310h1d65ade_0
+  - torchaudio=2.2.1=py310_cu118
+  - torchtriton=2.2.0=py310
+  - torchvision=0.17.1=py310_cu118
+  - tqdm=4.67.1=pyhd8ed1ab_1
   - pip:
-      - beartype==0.17.2
-      - biopandas==0.4.1
-      - biopython>=1.79
-      - dm-tree==0.1.8
-      - einops==0.7.0
+      - beartype==0.20.0
+      - biopandas==0.5.1
+      - biopython==1.79
+      - biotite==1.1.0
+      - git+https://github.com/NVIDIA/dllogger.git@0540a43971f4a8a16693a9de9de73c1072020769
+      - dm-tree==0.1.9
+      - einops==0.8.1
       - fair-esm==2.0.0
+      - fairscale==0.4.13
+      - gemmi==0.7.0
       - hydra-colorlog==1.2.0
-      - lightning==2.2.2
-      - lightning-utilities==0.11.1
-      - lovely-numpy==0.2.11
-      - lovely-tensors==0.1.15
-      - ml-collections==0.1.1
+      - hydra-core==1.3.2
+      - hydra-optuna-sweeper==1.2.0
+      - lightning==2.5.0.post0
+      - lightning-utilities==0.14.1
+      - lovely-numpy==0.2.13
+      - lovely-tensors==0.1.18
+      - ml-collections==1.0.0
+      - msgpack==1.1.0
       - msgpack-numpy==0.4.8
-      - numpy==1.23.5
+      - numpy==1.26.4
+      - omegaconf==2.3.0
       - git+https://github.com/amorehead/openfold.git@fe1275099639bf7e617e09ef24d6af778647dd64
-      - prody==2.4.1
-      - pytorch-lightning==2.2.2
+      - pandas==2.2.3
+      - plinder==0.2.24
+      - plotly==6.0.0
+      - posebusters==0.2.13
+      # - prody==2.4.1  # NOTE: we must `pip` install Prody to skip its NumPy dependency
+      - pytorch-lightning==2.5.0.post0
       - git+https://github.com/facebookresearch/pytorch3d.git@3da7703c5ac10039645966deddffe8db52eab8c5
+      - rich==13.9.4
       - rootutils==1.0.7
-      - torchmetrics==1.3.1
-      - wandb==0.17.0
+      - seaborn==0.13.2
+      - torchmetrics==1.6.3
+      - wandb==0.19.8
diff --git a/flowdock/data/components/plinder_dataset.py b/flowdock/data/components/plinder_dataset.py
new file mode 100644
index 0000000..9ff82aa
--- /dev/null
+++ b/flowdock/data/components/plinder_dataset.py
@@ -0,0 +1,337 @@
+import glob
+import os
+import random
+import tempfile
+from pathlib import Path
+
+import esm
+import numpy as np
+import pandas as pd
+import rootutils
+import torch
+from beartype.typing import Any, Callable, Dict
+from plinder.core.index.system import PlinderSystem
+from plinder.core.structure.structure import Structure
+from rdkit import Chem
+from torch.utils.data import Dataset
+
+rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+from flowdock.data.components.mol_features import process_molecule
+from flowdock.utils import RankedLogger
+from flowdock.utils.data_utils import (
+    centralize_complex_graph,
+    combine_molecules,
+    merge_protein_and_ligands,
+    pdb_filepath_to_protein,
+    process_protein,
+)
+from flowdock.utils.model_utils import extract_esm_embeddings, sample_inplace_to_torch
+
+log = RankedLogger(__name__, rank_zero_only=False)
+
+
+def flowdock_structure_featurizer(
+    apo_structure: Structure,
+    holo_structure: Structure,
+    chain_mapping: Dict[str, str],
+    esm_model: esm.ESM2,
+    esm_alphabet: esm.data.Alphabet,
+    esm_batch_converter: esm.data.BatchConverter,
+    esm_repr_layer: int,
+    n_lig_patches: int,
+) -> dict[str, Any]:
+    """Featurize Plinder apo and holo `Structure` objects and return a dictionary of features for
+    FlowDock.
+
+    :param apo_structure: The apo `Structure` object to featurize.
+    :param holo_structure: The holo `Structure` object to featurize.
+    :param chain_mapping: A dictionary mapping chain IDs from the holo structure to the apo structure.
+    :param esm_model: The ESM model to use for protein sequence featurization.
+    :param esm_alphabet: The ESM alphabet to use for protein sequence featurization.
+    :param esm_batch_converter: The ESM batch converter to use for protein sequence featurization.
+    :param esm_repr_layer: The ESM representation layer to use for protein sequence featurization.
+    :param n_lig_patches: The number of ligand patches to use.
+    :return: A dictionary of features for FlowDock.
+    """
+    try:
+        # match holo protein to apo protein
+        apo_structure.set_chain(holo_structure.protein_chain_ordered[0])
+        holo_structure, apo_structure = holo_structure.align_common_sequence(apo_structure)
+        apo_structure, apo_structure_raw_rmsd, apo_structure_refined_rmsd = (
+            apo_structure.superimpose(holo_structure)
+        )
+
+        with (
+            tempfile.NamedTemporaryFile(delete=False, suffix=".pdb") as tmp_apo_pdb,
+            tempfile.NamedTemporaryFile(delete=False, suffix=".pdb") as tmp_holo_pdb,
+        ):
+            apo_protein_pdb_filepath = tmp_apo_pdb.name
+            holo_protein_pdb_filepath = tmp_holo_pdb.name
+
+            map_chain_id = np.vectorize(lambda x: chain_mapping.get(x, x.split(".")[-1]))
+
+            apo_structure.protein_atom_array.chain_id = map_chain_id(
+                apo_structure.protein_atom_array.chain_id
+            )
+            holo_structure.protein_atom_array.chain_id = map_chain_id(
+                holo_structure.protein_atom_array.chain_id
+            )
+
+            apo_structure.save_to_disk(Path(apo_protein_pdb_filepath))
+            holo_structure.save_to_disk(Path(holo_protein_pdb_filepath))
+
+        # process ligands
+        ligands = [
+            Chem.MolFromMolFile(ligand_sdf) for ligand_sdf in holo_structure.ligand_sdfs.values()
+        ]
+        lig_samples = [
+            process_molecule(
+                ligand,
+                ref_conf_xyz=np.array(ligand.GetConformer().GetPositions()),
+                return_as_dict=True,
+            )
+            for ligand in ligands
+        ]
+        for lig_sample in lig_samples:
+            lig_sample["metadata"]["sample_ID"] = holo_structure.id
+
+        # process holo protein
+        holo_af_protein = pdb_filepath_to_protein(holo_protein_pdb_filepath)
+        holo_protein_sample = process_protein(
+            holo_af_protein,
+            sample_name=f"{holo_structure.id}_",
+        )
+        complex_graph = holo_protein_sample
+
+        if np.isnan(complex_graph["features"]["res_atom_positions"]).any():
+            raise ValueError(
+                f"NaN values found in holo protein sample for system {holo_structure.id}"
+            )
+
+        # process apo protein
+        apo_af_protein = pdb_filepath_to_protein(apo_protein_pdb_filepath)
+        if not np.array_equal(
+            np.int_(holo_af_protein.aatype),
+            np.int_(apo_af_protein.aatype),
+        ):
+            raise ValueError(
+                f"Apo and holo protein sequences do not match for system {holo_structure.id}"
+            )
+
+        # embed apo protein sequence with ESM2
+        sequences = [
+            "".join(np.array(list(chain_seq))[chain_mask])
+            for (_, chain_seq, chain_mask) in apo_af_protein.letter_sequences
+        ]
+        esm_embeddings = extract_esm_embeddings(
+            esm_model,
+            esm_alphabet,
+            esm_batch_converter,
+            sequences,
+            device="cpu",
+            esm_repr_layer=esm_repr_layer,
+        )
+        sequences_to_embeddings = {
+            f"{seq}:{i}": esm_embeddings[i].cpu().numpy() for i, seq in enumerate(sequences)
+        }
+
+        apo_protein_sample = process_protein(
+            apo_af_protein,
+            sample_name=f"{holo_structure.id}_",
+            sequences_to_embeddings=sequences_to_embeddings,
+        )
+
+        # merge holo and apo proteins
+        for key in complex_graph.keys():
+            for subkey, value in apo_protein_sample[key].items():
+                complex_graph[key]["apo_" + subkey] = value
+
+        complex_graph["metadata"]["sample_ID"] = holo_structure.id
+
+        if np.isnan(complex_graph["features"]["apo_res_atom_positions"]).any():
+            raise ValueError(
+                f"NaN values found in apo protein sample for system {holo_structure.id}"
+            )
+        if (
+            complex_graph["features"]["res_atom_positions"].shape
+            != complex_graph["features"]["apo_res_atom_positions"].shape
+        ):
+            raise ValueError(
+                f"Atom positions shape mismatch between holo and apo protein samples for system {holo_structure.id}"
+            )
+
+        # merge ligands with holo and apo proteins
+        complex_graph = merge_protein_and_ligands(
+            lig_samples,
+            complex_graph,
+            n_lig_patches=n_lig_patches,
+        )
+
+        complex_graph["metadata"]["mol"] = combine_molecules(ligands)
+        complex_graph = centralize_complex_graph(sample_inplace_to_torch(complex_graph))
+
+    except Exception as e:
+        raise e
+
+    return complex_graph
+
+
+class FlowDockPlinderDataset(Dataset):  # type: ignore
+    """Creates a dataset for FlowDock from Plinder systems.
+
+    Parameters
+    ----------
+    data_dir : str
+        The directory where the data is stored
+    split : str
+        The split to sample from
+    featurizer: Callable[
+            [Structure, int], dict[str, torch.Tensor]
+    ] = structure_featurizer,
+        Transformation to turn structure to input tensors
+    system_must_have_apo_or_pred : bool
+        Whether to filter systems that do not have apo or predicted structures
+    system_must_be_deposited_before : str | None
+        Filter systems that were deposited on or after a certain date
+    system_must_be_deposited_after : str | None
+        Filter systems that were deposited before a certain date
+    min_protein_length : int | None
+        Filter systems with protein sequence length less than this
+    max_protein_length : int | None
+        Filter systems with protein sequence length greater than this
+    **kwargs : Any
+        Additional keyword arguments to pass to the dataset
+    """
+
+    def __init__(
+        self,
+        data_dir: str,
+        split: str,
+        featurizer: Callable[
+            [
+                Structure,
+                Structure,
+                esm.ESM2,
+                esm.data.Alphabet,
+                esm.data.BatchConverter,
+                int,
+                int,
+            ],
+            torch.Tensor | dict[str, torch.Tensor],
+        ],
+        system_must_have_apo_or_pred: bool = True,
+        system_must_be_deposited_before: str | None = None,
+        system_must_be_deposited_after: str | None = None,
+        min_protein_length: int | None = None,
+        max_protein_length: int | None = None,
+        **kwargs: Any,
+    ):
+        df = pd.read_parquet(
+            glob.glob(os.path.join(data_dir, "*", "*", "*", "splits", "split.parquet"))[0]
+        )
+        split_df = df[
+            (df["split"] == split)
+            & (df["system_pass_validation_criteria"] == True)  # noqa: E712
+            & (df["system_pass_statistics_criteria"] == True)  # noqa: E712
+            & (df["system_has_apo_or_pred"] == system_must_have_apo_or_pred)  # noqa: E712
+        ]
+        assert (
+            len(split_df) > 0
+        ), f"No systems found for split '{split}' with apo or pred structures."
+
+        self._system_ids = list(set(split_df["system_id"]))
+        self._num_examples = len(self._system_ids)
+
+        self._featurizer = featurizer
+
+        self.system_must_be_deposited_before = system_must_be_deposited_before
+        self.system_must_be_deposited_after = system_must_be_deposited_after
+
+        self.min_protein_length = min_protein_length
+        self.max_protein_length = max_protein_length
+
+    def __len__(self) -> int:
+        """Get the number of examples in the dataset."""
+        return self._num_examples
+
+    def __getitem__(
+        self, index: int
+    ) -> dict[str, int | str | pd.DataFrame | dict[str, str | pd.DataFrame]]:
+        """Get a single item from the dataset.
+
+        :param index: The index of the item to retrieve.
+        :return: A dictionary containing the features of the complex graph.
+        """
+        try:
+            if not 0 <= index < self._num_examples:
+                raise IndexError(index)
+
+            s = PlinderSystem(system_id=self._system_ids[index])
+
+            if self.system_must_be_deposited_before is not None:
+                # filter out systems that were deposited on or after a certain date
+                if s.entry["release_date"] >= self.system_must_be_deposited_before:
+                    log.info(
+                        f"System {s.system_id} was deposited on or after {self.system_must_be_deposited_before}. Skipping..."
+                    )
+                    return self.__getitem__(np.random.randint(0, self._num_examples))
+
+            if self.system_must_be_deposited_after is not None:
+                # filter out systems that were deposited before a certain date
+                if s.entry["release_date"] <= self.system_must_be_deposited_after:
+                    log.info(
+                        f"System {s.system_id} was deposited on or before {self.system_must_be_deposited_after}. Skipping..."
+                    )
+                    return self.__getitem__(np.random.randint(0, self._num_examples))
+
+            if self.min_protein_length is not None:
+                # filter out systems with protein sequence length less than `min_protein_length`
+                if len(s.holo_structure.protein_sequence_from_structure) < self.min_protein_length:
+                    log.info(
+                        f"System {s.system_id} has holo protein length {len(s.holo_structure.protein_sequence_from_structure)} < {self.min_protein_length}. Skipping..."
+                    )
+                    return self.__getitem__(np.random.randint(0, self._num_examples))
+
+            if self.max_protein_length is not None:
+                # filter out systems with protein sequence length greater than `max_protein_length`
+                if len(s.holo_structure.protein_sequence_from_structure) > self.max_protein_length:
+                    log.info(
+                        f"System {s.system_id} has holo protein length {len(s.holo_structure.protein_sequence_from_structure)} > {self.max_protein_length}. Skipping..."
+                    )
+                    return self.__getitem__(np.random.randint(0, self._num_examples))
+
+            # prefer to load (random) crystal apo protein structures over (random) predicted apo protein structures
+            if any(struct.structure_type == "apo" for struct in s.alternate_structures.values()):
+                apo_structure = random.choice(  # nosec
+                    list(
+                        struct
+                        for struct in s.alternate_structures.values()
+                        if struct.structure_type == "apo"
+                    )
+                )
+            else:
+                assert s.alternate_structures, f"System {s.system_id} has no alternate structures."
+                apo_structure = random.choice(  # nosec
+                    list(
+                        struct
+                        for struct in s.alternate_structures.values()
+                        if struct.structure_type == "pred"
+                    )
+                )
+
+            holo_structure = s.holo_structure
+            complex_graph = self._featurizer(apo_structure, holo_structure, s.chain_mapping)
+
+            complex_graph["features"]["affinity"] = torch.tensor(
+                # NOTE: until https://github.com/plinder-org/plinder/issues/94 is fixed,
+                # PLINDER's binding affinity values cannot be used
+                [torch.nan for _ in range(complex_graph["metadata"]["num_molid"])],
+                dtype=torch.float32,
+            )
+
+        except Exception as e:
+            log.error(f"Skipping system at index {index} because of the error: {e}")
+            return self.__getitem__(np.random.randint(0, self._num_examples))
+
+        return complex_graph
diff --git a/flowdock/data/plinder_datamodule.py b/flowdock/data/plinder_datamodule.py
new file mode 100644
index 0000000..d9e3a2f
--- /dev/null
+++ b/flowdock/data/plinder_datamodule.py
@@ -0,0 +1,280 @@
+import os
+from functools import partial
+
+import esm
+import numpy as np
+import rootutils
+import torch
+from beartype.typing import Any, Dict, List, Literal, Optional
+from lightning import LightningDataModule
+from torch.utils.data import DataLoader, Dataset
+
+rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
+
+from flowdock.data.components.mol_features import collate_samples
+from flowdock.data.components.plinder_dataset import (
+    FlowDockPlinderDataset,
+    flowdock_structure_featurizer,
+)
+from flowdock.utils import RankedLogger
+
+log = RankedLogger(__name__, rank_zero_only=True)
+
+DATA_PHASE = Literal["train", "val", "test"]
+
+
+class PlinderDataModule(LightningDataModule):
+    """`LightningDataModule` for wrapping custom PlinderDatasets.
+
+    A `LightningDataModule` implements 7 key methods:
+
+    ```python
+        def prepare_data(self):
+        # Things to do on 1 GPU/TPU (not on every GPU/TPU in DDP).
+        # Download data, pre-process, split, save to disk, etc...
+
+        def setup(self, stage):
+        # Things to do on every process in DDP.
+        # Load data, set variables, etc...
+
+        def train_dataloader(self):
+        # return train dataloader
+
+        def val_dataloader(self):
+        # return validation dataloader
+
+        def test_dataloader(self):
+        # return test dataloader
+
+        def predict_dataloader(self):
+        # return predict dataloader
+
+        def teardown(self, stage):
+        # Called on every process in DDP.
+        # Clean up after fit or test.
+    ```
+
+    This allows you to share a full dataset without explaining how to download,
+    split, transform and process the data.
+
+    Read the docs:
+        https://lightning.ai/docs/pytorch/latest/data/datamodule.html
+    """
+
+    def __init__(
+        self,
+        data_dir: str = "data/PLINDER/",
+        batch_size: int = 16,
+        num_workers: int = 0,
+        pin_memory: bool = False,
+        stage: Optional[str] = None,
+        plinder_offline: bool = False,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a `PlinderDataModule`.
+
+        :param data_dir: The data directory. Defaults to `"data/"`.
+        :param batch_size: The batch size. Defaults to `16`.
+        :param num_workers: The number of workers. Defaults to `0`.
+        :param pin_memory: Whether to pin memory. Defaults to `False`.
+        :param plinder_offline: Whether to use the offline version of PLINDER. Defaults to `False`.
+        :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``.
+        """
+        super().__init__()
+
+        os.environ["PLINDER_MOUNT"] = os.path.abspath(data_dir)
+        os.environ["PLINDER_OFFLINE"] = str(plinder_offline)  # maybe avoid redownloading dataset
+
+        # this line allows to access init params with 'self.hparams' attribute
+        # also ensures init params will be stored in ckpt
+        self.save_hyperparameters(logger=False)
+
+        log.info("Loading pretrained ESM model...")
+        esm_model, self.esm_alphabet = esm.pretrained.load_model_and_alphabet_hub(
+            self.hparams.esm_version
+        )
+        self.esm_model = esm_model.eval().float()
+        self.esm_batch_converter = self.esm_alphabet.get_batch_converter()
+        self.esm_model.cpu()
+
+        featurizer = partial(
+            flowdock_structure_featurizer,
+            esm_model=self.esm_model,
+            esm_alphabet=self.esm_alphabet,
+            esm_batch_converter=self.esm_batch_converter,
+            esm_repr_layer=self.hparams.esm_repr_layer,
+            n_lig_patches=self.hparams.n_lig_patches,
+        )
+
+        # prepare for dataset(s) to be loaded into rank-shared memory (e.g., when setting `trainer.strategy = "ddp_spawn"`)
+        self.data_train = FlowDockPlinderDataset(
+            data_dir,
+            split="train",
+            featurizer=featurizer,
+            system_must_be_deposited_before="2018-01-01",  # filter out training systems deposited after this date
+            min_protein_length=self.hparams.min_protein_length,  # filter out training systems with protein sequence length less than this
+            max_protein_length=self.hparams.max_protein_length,  # filter out training systems with protein sequence length greater than this
+            use_alternate_structures=True,
+        )
+
+        self.data_val = FlowDockPlinderDataset(
+            data_dir,
+            split="val",
+            featurizer=featurizer,
+            system_must_be_deposited_after="2017-12-31",  # filter out validation systems deposited on or before this date
+            system_must_be_deposited_before="2019-01-01",  # filter out validation systems deposited after this date
+            min_protein_length=self.hparams.min_protein_length,  # filter out validation systems with protein sequence length less than this
+            max_protein_length=self.hparams.max_protein_length,  # filter out validation systems with protein sequence length greater than this
+            use_alternate_structures=True,
+        )
+
+        self.data_test = FlowDockPlinderDataset(
+            data_dir,
+            split="test",
+            featurizer=featurizer,
+            system_must_be_deposited_after="2018-12-31",  # filter out test systems deposited on or before this date
+            min_protein_length=self.hparams.min_protein_length,  # filter out test systems with protein sequence length less than this
+            max_protein_length=self.hparams.max_protein_length,  # filter out test systems with protein sequence length greater than this
+            use_alternate_structures=True,
+        )
+
+    def prepare_data(self) -> None:
+        """Download data if needed. Lightning ensures that `self.prepare_data()` is called only
+        within a single process on CPU, so you can safely add your downloading logic within. In
+        case of multi-node training, the execution of this hook depends upon
+        `self.prepare_data_per_node()`.
+
+        Do not use it to assign state (self.x = y).
+        """
+        pass
+
+    def setup(self, stage: Optional[str] = None) -> None:
+        """Load data. Set variables: `self.data_train`, `self.data_val`, `self.data_test`.
+
+        This method is called by Lightning before `trainer.fit()`, `trainer.validate()`, `trainer.test()`, and
+        `trainer.predict()`, so be careful not to execute things like random split twice! Also, it is called after
+        `self.prepare_data()` and there is a barrier in between which ensures that all the processes proceed to
+        `self.setup()` once the data is prepared and available for use.
+
+        :param stage: The stage to setup. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`. Defaults to ``None``.
+        """
+        pass
+
+    @staticmethod
+    def dynamic_batching_by_max_edge_count(
+        x: Dict[str, Any], max_n_edges: int, max_batch_size: int
+    ) -> Any:
+        """Dynamically batch by maximum edge count.
+
+        :param x: The input graph data. If `None`, skip the current batch.
+        :param max_n_edges: The maximum number of edges.
+        :param max_batch_size: The maximum batch size.
+        :return: The batched data.
+        """
+        if "num_u" in x["metadata"].keys():
+            num_edges_upperbound = (
+                x["metadata"]["num_a"] * 128 + x["metadata"]["num_i"] * 8 + 160**2
+            )
+        else:
+            num_edges_upperbound = x["metadata"]["num_a"] * 128 + 160**2
+        batch_size = max(1, min(max_n_edges // num_edges_upperbound, max_batch_size))
+        return collate_samples([x] * batch_size)
+
+    def get_dataloader(
+        self,
+        phase: DATA_PHASE,
+        dataset: Dataset,
+        **kwargs: Dict[str, Any],
+    ) -> DataLoader[Any]:
+        """Create a dataloader from a dataset.
+
+        :param phase: The phase of the dataset. Either `"train"`, `"val"`, or `"test"`.
+        :param dataset: The dataset.
+        :param kwargs: Additional keyword arguments to pass to the dataloader.
+        :return: The dataloader.
+        """
+        if phase == "train":
+            batch_size = self.hparams.batch_size
+            epoch_frac = self.hparams.epoch_frac
+        else:
+            batch_size = 1
+            epoch_frac = 1
+        sampled_indices = np.random.choice(
+            len(dataset),
+            int(len(dataset) * epoch_frac),
+            replace=False,
+        )
+        if phase == "val":
+            sampled_indices = np.repeat(sampled_indices, self.trainer.world_size)
+        subdataset = torch.utils.data.Subset(dataset, sampled_indices)
+        return DataLoader(
+            subdataset,
+            batch_size=None,
+            collate_fn=lambda x: self.dynamic_batching_by_max_edge_count(
+                x, self.hparams.edge_crop_size, batch_size
+            ),
+            num_workers=self.hparams.num_workers,
+            pin_memory=self.hparams.pin_memory,
+            **kwargs,
+        )
+
+    def train_dataloader(self) -> DataLoader[Any]:
+        """Create and return the train dataloader.
+
+        :return: The train dataloader.
+        """
+        return self.get_dataloader(
+            "train",
+            dataset=self.data_train,
+            shuffle=True,
+        )
+
+    def val_dataloader(self) -> List[DataLoader[Any]]:
+        """Create and return the validation dataloaders.
+
+        :return: The validation dataloaders.
+        """
+        return self.get_dataloader(
+            "val",
+            dataset=self.data_val,
+            shuffle=False,
+        )
+
+    def test_dataloader(self) -> List[DataLoader[Any]]:
+        """Create and return the test dataloaders.
+
+        :return: The test dataloaders.
+        """
+        return self.get_dataloader(
+            "test",
+            dataset=self.data_test,
+            shuffle=False,
+        )
+
+    def teardown(self, stage: Optional[str] = None) -> None:
+        """Lightning hook for cleaning up after `trainer.fit()`, `trainer.validate()`,
+        `trainer.test()`, and `trainer.predict()`.
+
+        :param stage: The stage being torn down. Either `"fit"`, `"validate"`, `"test"`, or `"predict"`.
+            Defaults to ``None``.
+        """
+        pass
+
+    def state_dict(self) -> Dict[Any, Any]:
+        """Called when saving a checkpoint. Implement to generate and save the datamodule state.
+
+        :return: A dictionary containing the datamodule state that you want to save.
+        """
+        return {}
+
+    def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+        """Called when loading a checkpoint. Implement to reload datamodule state given datamodule
+        `state_dict()`.
+
+        :param state_dict: The datamodule state returned by `self.state_dict()`.
+        """
+        pass
+
+
+if __name__ == "__main__":
+    _ = PlinderDataModule()
diff --git a/flowdock/utils/data_utils.py b/flowdock/utils/data_utils.py
index 6b7aa11..7229f41 100644
--- a/flowdock/utils/data_utils.py
+++ b/flowdock/utils/data_utils.py
@@ -27,8 +27,6 @@
 from rdkit.Chem import AllChem
 from rdkit.Geometry import Point3D
 
-from flowdock.data.components.process_mols import read_molecule
-
 rootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
 
 from flowdock.data.components import residue_constants
@@ -37,6 +35,7 @@
     collate_numpy_samples,
     process_mol_file,
 )
+from flowdock.data.components.process_mols import read_molecule
 from flowdock.data.components.residue_constants import restype_1to3 as af_restype_1to3
 from flowdock.data.components.residue_constants import restypes as af_restypes
 from flowdock.models.components.transforms import LatentCoordinateConverter
@@ -1216,7 +1215,7 @@ def convert_protein_pts_to_pdb(
         chain = Chain(chain_id)
         model.add(chain)
 
-        protein_chain_data = torch.load(processed_pt_filename)
+        protein_chain_data = torch.load(processed_pt_filename)  # nosec
         for residue_id in range(len(protein_chain_data["seq"])):
             aa = Polypeptide.one_to_three(protein_chain_data["seq"][residue_id])
             aa_atoms = AA_TO_LONG[AA_TO_NUM[aa]][:14]
diff --git a/notebooks/casp16_flowdock_bust_results.csv b/notebooks/casp16_flowdock_bust_results.csv
new file mode 100644
index 0000000..451670e
--- /dev/null
+++ b/notebooks/casp16_flowdock_bust_results.csv
@@ -0,0 +1,302 @@
+mol_pred_loaded,mol_true_loaded,mol_cond_loaded,sanitization,inchi_convertible,all_atoms_connected,molecular_formula,molecular_bonds,double_bond_stereochemistry,tetrahedral_chirality,bond_lengths,bond_angles,internal_steric_clash,aromatic_ring_flatness,non-aromatic_ring_non-flatness,double_bond_flatness,internal_energy,protein-ligand_maximum_distance,minimum_distance_to_protein,minimum_distance_to_organic_cofactors,minimum_distance_to_inorganic_cofactors,minimum_distance_to_waters,volume_overlap_with_protein,volume_overlap_with_organic_cofactors,volume_overlap_with_inorganic_cofactors,volume_overlap_with_waters,rmsd_≤_2å,passes_valence_checks,passes_kekulization,inchi_crystal_valid,inchi_docked_valid,inchi_crystal,inchi_docked,inchi_overall,inchi_version,hydrogens,net_charge,protons,stereo_sp3,stereo_sp3_inverted,stereo_type,stereochemistry_preserved,number_bonds,shortest_bond_relative_length,longest_bond_relative_length,number_short_outlier_bonds,number_long_outlier_bonds,number_angles,most_extreme_relative_angle,number_outlier_angles,number_noncov_pairs,shortest_noncovalent_relative_distance,number_clashes,number_valid_bonds,number_valid_angles,number_valid_noncov_pairs,number_aromatic_rings_checked,number_aromatic_rings_pass,aromatic_ring_maximum_distance_from_plane,number_non-aromatic_rings_checked,number_non-aromatic_rings_pass,non-aromatic_ring_maximum_distance_from_plane,number_double_bonds_checked,number_double_bonds_pass,double_bond_maximum_distance_from_plane,ensemble_avg_energy,mol_pred_energy,energy_ratio,relative_deviation,z_value,relative_deviation_passes,z_value_passes,smallest_distance_protein,num_pairwise_clashes_protein,most_extreme_ligand_atom_id_protein,most_extreme_protein_atom_id_protein,most_extreme_ligand_element_protein,most_extreme_protein_element_protein,most_extreme_ligand_vdw_protein,most_extreme_protein_vdw_protein,most_extreme_sum_radii_protein,most_extreme_distance_protein,most_extreme_sum_radii_scaled_protein,most_extreme_relative_distance_protein,most_extreme_clash_protein,smallest_distance_organic_cofactors,not_too_far_away_organic_cofactors,num_pairwise_clashes_organic_cofactors,most_extreme_ligand_atom_id_organic_cofactors,most_extreme_protein_atom_id_organic_cofactors,most_extreme_ligand_element_organic_cofactors,most_extreme_protein_element_organic_cofactors,most_extreme_ligand_vdw_organic_cofactors,most_extreme_protein_vdw_organic_cofactors,most_extreme_sum_radii_organic_cofactors,most_extreme_distance_organic_cofactors,most_extreme_sum_radii_scaled_organic_cofactors,most_extreme_relative_distance_organic_cofactors,most_extreme_clash_organic_cofactors,smallest_distance_inorganic_cofactors,not_too_far_away_inorganic_cofactors,num_pairwise_clashes_inorganic_cofactors,most_extreme_ligand_atom_id_inorganic_cofactors,most_extreme_protein_atom_id_inorganic_cofactors,most_extreme_ligand_element_inorganic_cofactors,most_extreme_protein_element_inorganic_cofactors,most_extreme_ligand_vdw_inorganic_cofactors,most_extreme_protein_vdw_inorganic_cofactors,most_extreme_sum_radii_inorganic_cofactors,most_extreme_distance_inorganic_cofactors,most_extreme_sum_radii_scaled_inorganic_cofactors,most_extreme_relative_distance_inorganic_cofactors,most_extreme_clash_inorganic_cofactors,smallest_distance_waters,not_too_far_away_waters,num_pairwise_clashes_waters,most_extreme_ligand_atom_id_waters,most_extreme_protein_atom_id_waters,most_extreme_ligand_element_waters,most_extreme_protein_element_waters,most_extreme_ligand_vdw_waters,most_extreme_protein_vdw_waters,most_extreme_sum_radii_waters,most_extreme_distance_waters,most_extreme_sum_radii_scaled_waters,most_extreme_relative_distance_waters,most_extreme_clash_waters,volume_overlap_protein,volume_overlap_organic_cofactors,volume_overlap_inorganic_cofactors,volume_overlap_waters,rmsd,kabsch_rmsd,centroid_distance,mol_id,lig_idx
+True,True,True,True,True,True,False,True,True,False,True,True,False,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C25H45FN2O4/c1-3-32-25(31)27(2)15-21-20-13-18(26)11-12-22(20)28(23(21)24(29)30)14-17-9-6-8-16-7-4-5-10-19(16)17/h16-25,29-31H,3-15H2,1-2H3/t16-,17-,18-,19-,20+,21-,22-,23-,25-/m1/s1","InChI=1S/C25H23FN2O4/c1-3-32-25(31)27(2)15-21-20-13-18(26)11-12-22(20)28(23(21)24(29)30)14-17-9-6-8-16-7-4-5-10-19(16)17/h4-13H,3,14-15H2,1-2H3,(H,29,30)",False,True,False,True,True,False,False,False,False,35.0,0.888336916855402,1.0630302083725751,0.0,0.0,50.0,1.1160263165642479,0.0,411.0,0.6647106377610209,1.0,35.0,50.0,410.0,3,3,0.06932859312036853,0,0,,0,0,,94.72889122271654,5664.062868083437,59.7923484057962,58.7923484057962,1720.7797881649458,True,False,2.613643529634445,0,27,1674,C,O,1.7,1.55,3.25,2.613643529634445,3.25,0.8041980091182908,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.006766815536608472,,,,7.647965228677658,2.544445465919415,1.7610375158514686,L1001,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,True,True,True,True,False,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C32H58N2O3/c1-3-21-14-15-24-25(16-17-33-20(2)35)31(34-27(24)18-21)29(23-12-8-5-9-13-23)30-28(36)19-26(32(30)37)22-10-6-4-7-11-22/h20-37H,3-19H2,1-2H3/t20-,21+,24+,25-,26+,27+,28-,29+,30+,31+,32+/m0/s1","InChI=1S/C32H32N2O3/c1-3-21-14-15-24-25(16-17-33-20(2)35)31(34-27(24)18-21)29(23-12-8-5-9-13-23)30-28(36)19-26(32(30)37)22-10-6-4-7-11-22/h4-15,18,26,29,34,37H,3,16-17,19H2,1-2H3,(H,33,35)/t26-,29-/m1/s1",False,True,False,True,True,False,False,True,False,41.0,0.9072080446239937,1.0993054461565652,0.0,0.0,58.0,1.10037307524872,0.0,567.0,0.8764508704337198,0.0,41.0,58.0,567.0,3,3,0.05914412427994778,1,1,0.07891078356494191,1,1,0.12568540782274973,110.11975240364386,482.62719210607514,4.382748612955518,3.3827486129555173,56.020731531156734,True,True,2.346167670905044,1,0,1750,C,C,1.7,1.7,3.4,2.346167670905044,3.4,0.6900493149720718,True,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.007538253825382538,,,,3.0188929421587996,1.346839731351946,1.1675948623373693,L1002,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C21H36F4N2O2S2/c1-12-16-11-15(22)3-5-19(16)30-20(12)31(28,29)27-18-4-2-14(10-17(18)21(23,24)25)13-6-8-26-9-7-13/h12-20,26-29H,2-11H2,1H3/t12-,14-,15-,16+,17-,18+,19-,20-/m1/s1","InChI=1S/C21H14F4N2O2S2/c1-12-16-11-15(22)3-5-19(16)30-20(12)31(28,29)27-18-4-2-14(10-17(18)21(23,24)25)13-6-8-26-9-7-13/h2-11,27H,1H3",False,True,False,True,True,False,False,False,False,34.0,0.9330444948222727,1.0756846185554914,0.0,0.0,52.0,1.0821735177365235,0.0,379.0,0.9462177075077045,0.0,34.0,52.0,379.0,3,3,0.0853899067997181,0,0,,0,0,,92.885457656126,221.1836595159479,2.381251759934246,1.381251759934246,114.9052745025256,True,False,2.7821972647531665,0,5,1563,O,C,1.55,1.7,3.25,2.7821972647531665,3.25,0.8560606968471282,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.006096734859775098,,,,7.402571987927683,1.1638851471425036,5.928543818696811,L1003,0
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+True,True,True,True,True,True,False,True,True,False,True,True,True,True,False,True,True,True,False,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C16H30BrN3O2/c1-11(12-5-3-2-4-6-12)19-10-15(21)20(16(19)22)14-7-13(17)8-18-9-14/h11-16,18,21-22H,2-10H2,1H3/t11-,13-,14+,15+,16+/m1/s1","InChI=1S/C16H14BrN3O2/c1-11(12-5-3-2-4-6-12)19-10-15(21)20(16(19)22)14-7-13(17)8-18-9-14/h2-9,11H,10H2,1H3/t11-/m1/s1",False,True,False,True,True,False,True,True,False,24.0,0.9196938719715028,1.0761245423418215,0.0,0.0,34.0,1.0717033448734121,0.0,173.0,0.9648481803983676,0.0,24.0,34.0,173.0,2,2,0.09128094944546271,1,0,0.04171046226621441,0,0,,61.502914457369016,202.02449573338006,3.2847954851540266,2.2847954851540266,1243.106494299084,True,False,2.0840250718261513,1,18,1280,Br,N,1.9,1.6,3.5,2.0840250718261513,3.5,0.5954357348074718,True,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.024480712166172106,,,,4.461248697241206,0.4702934724153606,1.4702021892560937,L4027,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,False,True,True,True,False,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C16H30BrN3O2/c1-11(12-5-3-2-4-6-12)19-10-15(21)20(16(19)22)14-7-13(17)8-18-9-14/h11-16,18,21-22H,2-10H2,1H3/t11-,13-,14+,15+,16-/m1/s1","InChI=1S/C16H14BrN3O2/c1-11(12-5-3-2-4-6-12)19-10-15(21)20(16(19)22)14-7-13(17)8-18-9-14/h2-9,11H,10H2,1H3/t11-/m1/s1",False,True,False,True,True,False,True,True,False,24.0,0.9196938719715028,1.0761245423418215,0.0,0.0,34.0,1.0717033448734121,0.0,173.0,0.9648481803983676,0.0,24.0,34.0,173.0,2,2,0.09128094944546271,1,0,0.04171046226621441,0,0,,61.502914457369016,202.02449573338006,3.2847954851540266,2.2847954851540266,1243.106494299084,True,False,2.0840250718261513,1,18,1280,Br,N,1.9,1.6,3.5,2.0840250718261513,3.5,0.5954357348074718,True,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.024480712166172106,,,,45.83087354802437,0.5238127513475109,45.47610907557659,L4027,1
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,False,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C15H26BrF2N3O2/c16-10-4-11(6-19-5-10)21-14(22)8-20(15(21)23)7-9-1-2-12(17)13(18)3-9/h9-15,19,22-23H,1-8H2/t9-,10+,11-,12+,13+,14-,15+/m0/s1","InChI=1S/C15H10BrF2N3O2/c16-10-4-11(6-19-5-10)21-14(22)8-20(15(21)23)7-9-1-2-12(17)13(18)3-9/h1-6H,7-8H2",False,True,False,True,True,False,False,False,False,25.0,0.932415535118016,1.0844031884608907,0.0,0.0,36.0,1.057334232566248,0.0,192.0,0.956211471035083,0.0,25.0,36.0,192.0,2,2,0.03279437478943025,1,0,0.04099255548435538,0,0,,57.63618502259155,146.7947037400872,2.546919156473461,1.546919156473461,52220.571615297544,True,False,2.8392149073291377,0,14,3487,O,S,1.55,1.8,3.35,2.8392149073291377,3.35,0.8475268380086978,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.004206291148500366,,,,46.75674917485175,2.1159097999627523,46.68695590594273,L4028,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,False,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C15H26BrF2N3O2/c16-10-4-11(6-19-5-10)21-14(22)8-20(15(21)23)7-9-1-2-12(17)13(18)3-9/h9-15,19,22-23H,1-8H2/t9-,10+,11-,12+,13+,14-,15+/m0/s1","InChI=1S/C15H10BrF2N3O2/c16-10-4-11(6-19-5-10)21-14(22)8-20(15(21)23)7-9-1-2-12(17)13(18)3-9/h1-6H,7-8H2",False,True,False,True,True,False,False,False,False,25.0,0.932415535118016,1.0844031884608907,0.0,0.0,36.0,1.057334232566248,0.0,192.0,0.956211471035083,0.0,25.0,36.0,192.0,2,2,0.03279437478943025,1,0,0.04099255548435538,0,0,,57.63618502259155,146.7947037400872,2.546919156473461,1.546919156473461,52220.571615297544,True,False,2.8392149073291377,0,14,3487,O,S,1.55,1.8,3.35,2.8392149073291377,3.35,0.8475268380086978,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.004206291148500366,,,,7.404898541418082,1.9471716779888102,1.3718386132602343,L4028,1
diff --git a/notebooks/casp16_flowdock_bust_results_relaxed.csv b/notebooks/casp16_flowdock_bust_results_relaxed.csv
new file mode 100644
index 0000000..034a9f3
--- /dev/null
+++ b/notebooks/casp16_flowdock_bust_results_relaxed.csv
@@ -0,0 +1,302 @@
+mol_pred_loaded,mol_true_loaded,mol_cond_loaded,sanitization,inchi_convertible,all_atoms_connected,molecular_formula,molecular_bonds,double_bond_stereochemistry,tetrahedral_chirality,bond_lengths,bond_angles,internal_steric_clash,aromatic_ring_flatness,non-aromatic_ring_non-flatness,double_bond_flatness,internal_energy,protein-ligand_maximum_distance,minimum_distance_to_protein,minimum_distance_to_organic_cofactors,minimum_distance_to_inorganic_cofactors,minimum_distance_to_waters,volume_overlap_with_protein,volume_overlap_with_organic_cofactors,volume_overlap_with_inorganic_cofactors,volume_overlap_with_waters,rmsd_≤_2å,passes_valence_checks,passes_kekulization,inchi_crystal_valid,inchi_docked_valid,inchi_crystal,inchi_docked,inchi_overall,inchi_version,hydrogens,net_charge,protons,stereo_sp3,stereo_sp3_inverted,stereo_type,stereochemistry_preserved,number_bonds,shortest_bond_relative_length,longest_bond_relative_length,number_short_outlier_bonds,number_long_outlier_bonds,number_angles,most_extreme_relative_angle,number_outlier_angles,number_noncov_pairs,shortest_noncovalent_relative_distance,number_clashes,number_valid_bonds,number_valid_angles,number_valid_noncov_pairs,number_aromatic_rings_checked,number_aromatic_rings_pass,aromatic_ring_maximum_distance_from_plane,number_non-aromatic_rings_checked,number_non-aromatic_rings_pass,non-aromatic_ring_maximum_distance_from_plane,number_double_bonds_checked,number_double_bonds_pass,double_bond_maximum_distance_from_plane,ensemble_avg_energy,mol_pred_energy,energy_ratio,relative_deviation,z_value,relative_deviation_passes,z_value_passes,smallest_distance_protein,num_pairwise_clashes_protein,most_extreme_ligand_atom_id_protein,most_extreme_protein_atom_id_protein,most_extreme_ligand_element_protein,most_extreme_protein_element_protein,most_extreme_ligand_vdw_protein,most_extreme_protein_vdw_protein,most_extreme_sum_radii_protein,most_extreme_distance_protein,most_extreme_sum_radii_scaled_protein,most_extreme_relative_distance_protein,most_extreme_clash_protein,smallest_distance_organic_cofactors,not_too_far_away_organic_cofactors,num_pairwise_clashes_organic_cofactors,most_extreme_ligand_atom_id_organic_cofactors,most_extreme_protein_atom_id_organic_cofactors,most_extreme_ligand_element_organic_cofactors,most_extreme_protein_element_organic_cofactors,most_extreme_ligand_vdw_organic_cofactors,most_extreme_protein_vdw_organic_cofactors,most_extreme_sum_radii_organic_cofactors,most_extreme_distance_organic_cofactors,most_extreme_sum_radii_scaled_organic_cofactors,most_extreme_relative_distance_organic_cofactors,most_extreme_clash_organic_cofactors,smallest_distance_inorganic_cofactors,not_too_far_away_inorganic_cofactors,num_pairwise_clashes_inorganic_cofactors,most_extreme_ligand_atom_id_inorganic_cofactors,most_extreme_protein_atom_id_inorganic_cofactors,most_extreme_ligand_element_inorganic_cofactors,most_extreme_protein_element_inorganic_cofactors,most_extreme_ligand_vdw_inorganic_cofactors,most_extreme_protein_vdw_inorganic_cofactors,most_extreme_sum_radii_inorganic_cofactors,most_extreme_distance_inorganic_cofactors,most_extreme_sum_radii_scaled_inorganic_cofactors,most_extreme_relative_distance_inorganic_cofactors,most_extreme_clash_inorganic_cofactors,smallest_distance_waters,not_too_far_away_waters,num_pairwise_clashes_waters,most_extreme_ligand_atom_id_waters,most_extreme_protein_atom_id_waters,most_extreme_ligand_element_waters,most_extreme_protein_element_waters,most_extreme_ligand_vdw_waters,most_extreme_protein_vdw_waters,most_extreme_sum_radii_waters,most_extreme_distance_waters,most_extreme_sum_radii_scaled_waters,most_extreme_relative_distance_waters,most_extreme_clash_waters,volume_overlap_protein,volume_overlap_organic_cofactors,volume_overlap_inorganic_cofactors,volume_overlap_waters,rmsd,kabsch_rmsd,centroid_distance,mol_id,lig_idx
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C25H45FN2O4/c1-3-32-25(31)27(2)15-21-20-13-18(26)11-12-22(20)28(23(21)24(29)30)14-17-9-6-8-16-7-4-5-10-19(16)17/h16-25,29-31H,3-15H2,1-2H3/t16-,17-,18-,19-,20+,21-,22-,23-,25-/m1/s1","InChI=1S/C25H23FN2O4/c1-3-32-25(31)27(2)15-21-20-13-18(26)11-12-22(20)28(23(21)24(29)30)14-17-9-6-8-16-7-4-5-10-19(16)17/h4-13H,3,14-15H2,1-2H3,(H,29,30)",False,True,False,True,True,False,False,False,False,35.0,0.9788064655171336,1.0515778472997035,0.0,0.0,50.0,1.0528937116278436,0.0,411.0,0.7904867086490855,0.0,35.0,50.0,411.0,3,3,0.02862166856995333,0,0,,0,0,,94.72889122271654,143.3133288518128,1.5128787743843604,0.5128787743843605,15.011331451635092,True,True,3.1105575159446883,0,4,1566,O,N,1.55,1.6,3.1500000000000004,3.1105575159446883,3.1500000000000004,0.9874785764903772,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.0017213983050847457,,,,7.601859216295381,2.598228249605375,2.65008205446003,L1001,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C32H58N2O3/c1-3-21-14-15-24-25(16-17-33-20(2)35)31(34-27(24)18-21)29(23-12-8-5-9-13-23)30-28(36)19-26(32(30)37)22-10-6-4-7-11-22/h20-37H,3-19H2,1-2H3/t20-,21+,24+,25-,26+,27+,28-,29+,30+,31+,32+/m0/s1","InChI=1S/C32H32N2O3/c1-3-21-14-15-24-25(16-17-33-20(2)35)31(34-27(24)18-21)29(23-12-8-5-9-13-23)30-28(36)19-26(32(30)37)22-10-6-4-7-11-22/h4-15,18,26,29,34,37H,3,16-17,19H2,1-2H3,(H,33,35)/t26-,29-/m1/s1",False,True,False,True,True,False,False,True,False,41.0,0.9787644097688488,1.0588825020939556,0.0,0.0,58.0,1.0455522997253386,0.0,567.0,0.9882225857603191,0.0,41.0,58.0,567.0,3,3,0.009929363034191896,1,1,0.09268114423504147,1,1,0.11751751676508562,110.11975240364386,146.41996435225462,1.329643058182263,0.32964305818226297,5.459124332447363,True,True,2.8590395677569767,0,17,506,O,N,1.55,1.6,3.1500000000000004,2.8590395677569767,3.1500000000000004,0.9076316088117385,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.002477756504110823,,,,3.5274110826420486,0.8890820793576906,2.0177261714347257,L1002,0
+True,True,True,True,True,True,False,True,True,False,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,True,False,True,True,True,True,"InChI=1S/C21H36F4N2O2S2/c1-12-16-11-15(22)3-5-19(16)30-20(12)31(28,29)27-18-4-2-14(10-17(18)21(23,24)25)13-6-8-26-9-7-13/h12-20,26-29H,2-11H2,1H3/t12-,14-,15-,16+,17-,18+,19-,20-/m1/s1","InChI=1S/C21H14F4N2O2S2/c1-12-16-11-15(22)3-5-19(16)30-20(12)31(28,29)27-18-4-2-14(10-17(18)21(23,24)25)13-6-8-26-9-7-13/h2-11,27H,1H3",False,True,False,True,True,False,False,False,False,34.0,0.9835349520064703,1.0570798845795233,0.0,0.0,52.0,1.0641847511858815,0.0,379.0,0.9596442820689794,0.0,34.0,52.0,379.0,3,3,0.025908994269180943,0,0,,0,0,,92.885457656126,131.76419152143245,1.4185664241354181,0.41856642413541817,34.82021978752834,True,True,3.0448462062967967,0,22,372,F,N,1.5,1.6,3.1,3.0448462062967967,3.1,0.9822084536441279,False,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,,False,0,,,,,,,,,,,,0.0020405387022173855,,,,7.914510638490177,1.0673827879517628,6.281258868245834,L1003,0
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diff --git a/notebooks/casp16_flowdock_vs_multicom_ligand_multi_ligand_structure_prediction_results_comparison_n=126.png b/notebooks/casp16_flowdock_vs_multicom_ligand_multi_ligand_structure_prediction_results_comparison_n=126.png
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diff --git a/notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb b/notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb
new file mode 100644
index 0000000..61c667f
--- /dev/null
+++ b/notebooks/casp16_flowdock_vs_multicom_ligand_structure_prediction_results_plotting.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## CASP16 FlowDock vs. MULTICOM_ligand Structure Prediction Results Plotting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CASP16 Structure Prediction Results #\n",
+    "\n",
+    "# Prepare FlowDock and MULTICOM_ligand's results\n",
+    "flowdock_top_ranked_results_df = pd.read_csv(\"casp16_flowdock_bust_results.csv\")\n",
+    "flowdock_top_ranked_results_relaxed_df = pd.read_csv(\"casp16_flowdock_bust_results_relaxed.csv\")\n",
+    "\n",
+    "multicom_ligand_results_df = pd.read_csv(\"casp16_multicom_ligand_pose_results.csv\")\n",
+    "multicom_ligand_top_ranked_results_df = multicom_ligand_results_df[\n",
+    "    (multicom_ligand_results_df[\"group\"] == \"LG207\")\n",
+    "    & (multicom_ligand_results_df[\"model\"].str.contains(\"_1\"))\n",
+    "]\n",
+    "\n",
+    "# Find common targets between FlowDock and MULTICOM_ligand's completed predictions\n",
+    "common_targets = (\n",
+    "    set(flowdock_top_ranked_results_df[\"mol_id\"])\n",
+    "    .intersection(set(flowdock_top_ranked_results_relaxed_df[\"mol_id\"]))\n",
+    "    .intersection(set(multicom_ligand_top_ranked_results_df[\"target\"]))\n",
+    ")\n",
+    "flowdock_top_ranked_results_df = flowdock_top_ranked_results_df[\n",
+    "    flowdock_top_ranked_results_df[\"mol_id\"].isin(common_targets)\n",
+    "]\n",
+    "flowdock_top_ranked_results_relaxed_df = flowdock_top_ranked_results_relaxed_df[\n",
+    "    flowdock_top_ranked_results_relaxed_df[\"mol_id\"].isin(common_targets)\n",
+    "]\n",
+    "multicom_ligand_top_ranked_results_df = multicom_ligand_top_ranked_results_df[\n",
+    "    multicom_ligand_top_ranked_results_df[\"target\"].isin(common_targets)\n",
+    "]\n",
+    "\n",
+    "# Plot FlowDock vs. MULTICOM_ligand's results as a histogram comparison\n",
+    "n = max(len(flowdock_top_ranked_results_df), len(flowdock_top_ranked_results_relaxed_df))\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "ax = sns.histplot(\n",
+    "    flowdock_top_ranked_results_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"green\",\n",
+    "    label=\"FlowDock (no post-processing)\",\n",
+    ")\n",
+    "ax = sns.histplot(\n",
+    "    flowdock_top_ranked_results_relaxed_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"orange\",\n",
+    "    label=\"FlowDock (with relaxation)\",\n",
+    ")\n",
+    "ax = sns.histplot(\n",
+    "    multicom_ligand_top_ranked_results_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"skyblue\",\n",
+    "    label=\"MULTICOM_ligand (with relaxation)\",\n",
+    ")\n",
+    "plt.xlabel(\"RMSD\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.xlim(0, 10)\n",
+    "plt.legend(title=\"Method\")\n",
+    "plt.title(\"\")\n",
+    "plt.savefig(\n",
+    "    f\"casp16_flowdock_vs_multicom_ligand_structure_prediction_results_comparison_n={n}.png\"\n",
+    ")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make plot (for multi-ligands only)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find results for single-ligand targets only\n",
+    "common_multi_ligand_targets = common_targets.intersection(\n",
+    "    set(\n",
+    "        flowdock_top_ranked_results_df[\"mol_id\"]\n",
+    "        .value_counts()[flowdock_top_ranked_results_df[\"mol_id\"].value_counts() > 1]\n",
+    "        .index\n",
+    "    ).intersection(\n",
+    "        set(\n",
+    "            flowdock_top_ranked_results_relaxed_df[\"mol_id\"]\n",
+    "            .value_counts()[flowdock_top_ranked_results_relaxed_df[\"mol_id\"].value_counts() > 1]\n",
+    "            .index\n",
+    "        )\n",
+    "    )\n",
+    ")\n",
+    "flowdock_top_ranked_multi_ligand_results_df = flowdock_top_ranked_results_df[\n",
+    "    flowdock_top_ranked_results_df[\"mol_id\"].isin(common_multi_ligand_targets)\n",
+    "]\n",
+    "flowdock_top_ranked_results_relaxed_multi_ligand_df = flowdock_top_ranked_results_relaxed_df[\n",
+    "    flowdock_top_ranked_results_relaxed_df[\"mol_id\"].isin(common_multi_ligand_targets)\n",
+    "]\n",
+    "multicom_ligand_top_ranked_multi_ligand_results_df = multicom_ligand_top_ranked_results_df[\n",
+    "    multicom_ligand_top_ranked_results_df[\"target\"].isin(common_multi_ligand_targets)\n",
+    "]\n",
+    "\n",
+    "# Plot FlowDock vs. MULTICOM_ligand's multi-ligand results as a histogram comparison\n",
+    "n = max(\n",
+    "    len(flowdock_top_ranked_multi_ligand_results_df),\n",
+    "    len(flowdock_top_ranked_results_relaxed_multi_ligand_df),\n",
+    ")\n",
+    "\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "ax = sns.histplot(\n",
+    "    flowdock_top_ranked_multi_ligand_results_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"green\",\n",
+    "    label=\"FlowDock (no post-processing)\",\n",
+    ")\n",
+    "ax = sns.histplot(\n",
+    "    flowdock_top_ranked_results_relaxed_multi_ligand_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"orange\",\n",
+    "    label=\"FlowDock (with relaxation)\",\n",
+    ")\n",
+    "ax = sns.histplot(\n",
+    "    multicom_ligand_top_ranked_multi_ligand_results_df[\"rmsd\"],\n",
+    "    bins=100,\n",
+    "    kde=True,\n",
+    "    color=\"skyblue\",\n",
+    "    label=\"MULTICOM_ligand (with relaxation)\",\n",
+    ")\n",
+    "plt.xlabel(\"RMSD\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.xlim(0, 10)\n",
+    "plt.legend(title=\"Method\")\n",
+    "plt.title(\"\")\n",
+    "plt.savefig(\n",
+    "    f\"casp16_flowdock_vs_multicom_ligand_multi_ligand_structure_prediction_results_comparison_n={n}.png\"\n",
+    ")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "PyMOL-MULTICOM_ligand",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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diff --git a/notebooks/casp16_multicom_ligand_pose_results.csv b/notebooks/casp16_multicom_ligand_pose_results.csv
new file mode 100644
index 0000000..8ecd6a3
--- /dev/null
+++ b/notebooks/casp16_multicom_ligand_pose_results.csv
@@ -0,0 +1,30222 @@
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diff --git a/notebooks/dockgen_structure_prediction_results_plotting.ipynb b/notebooks/dockgen_structure_prediction_results_plotting.ipynb
index eadcda8..40a4be6 100644
--- a/notebooks/dockgen_structure_prediction_results_plotting.ipynb
+++ b/notebooks/dockgen_structure_prediction_results_plotting.ipynb
@@ -68,6 +68,7 @@
     "    \"neuralplexer\",\n",
     "    \"flowdock_hp\",\n",
     "    \"flowdock_aft\",\n",
+    "    \"flowdock_pft\",\n",
     "    \"flowdock_esmfold\",\n",
     "    \"flowdock\",\n",
     "]\n",
@@ -85,6 +86,7 @@
     "globals()[\"neuralplexer_output_dir\"] = os.path.join(\"..\", \"forks\", \"NeuralPLexer\", \"inference\")\n",
     "globals()[\"flowdock_hp_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"hp_inference\")\n",
     "globals()[\"flowdock_aft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"aft_inference\")\n",
+    "globals()[\"flowdock_pft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"pft_inference\")\n",
     "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n",
     "    \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n",
     ")\n",
@@ -209,6 +211,20 @@
     "        )\n",
     "    )\n",
     "\n",
+    "    # FlowDock-PFT results\n",
+    "    globals()[f\"flowdock_pft_dockgen_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"flowdock_pft_output_dir\"],\n",
+    "        f\"flowdock_dockgen_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"flowdock_pft_dockgen_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_pft_output_dir\"],\n",
+    "            f\"flowdock_dockgen_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "    # FlowDock-ESMFold results\n",
     "    globals()[f\"flowdock_esmfold_dockgen_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
     "        globals()[\"flowdock_esmfold_output_dir\"],\n",
@@ -246,6 +262,7 @@
     "    \"neuralplexer\": \"NeuralPLexer\",\n",
     "    \"flowdock_hp\": \"FlowDock-HP\",\n",
     "    \"flowdock_aft\": \"FlowDock-AFT\",\n",
+    "    \"flowdock_pft\": \"FlowDock-PFT\",\n",
     "    \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n",
     "    \"flowdock\": \"FlowDock-AF3\",\n",
     "}\n",
@@ -260,6 +277,7 @@
     "    \"neuralplexer\": \"DL-based blind\",\n",
     "    \"flowdock_hp\": \"DL-based blind\",\n",
     "    \"flowdock_aft\": \"DL-based blind\",\n",
+    "    \"flowdock_pft\": \"DL-based blind\",\n",
     "    \"flowdock_esmfold\": \"DL-based blind\",\n",
     "    \"flowdock\": \"DL-based blind\",\n",
     "}\n",
@@ -534,7 +552,7 @@
     "colors = [\"#FB8072\", \"#BEBADA\"]\n",
     "\n",
     "bar_width = 0.5\n",
-    "r1 = [item - 0.25 for item in range(2, 24, 2)]\n",
+    "r1 = [item - 0.25 for item in range(2, 26, 2)]\n",
     "r2 = [x + bar_width for x in r1]\n",
     "\n",
     "(\n",
@@ -730,8 +748,8 @@
     "dockgen_relaxed_rmsd_lt_2_and_pb_valid_data_std.fillna(0, inplace=True)\n",
     "\n",
     "# define font properties\n",
-    "plt.rcParams[\"font.size\"] = 24\n",
-    "plt.rcParams[\"axes.labelsize\"] = 26\n",
+    "plt.rcParams[\"font.size\"] = 22\n",
+    "plt.rcParams[\"axes.labelsize\"] = 24\n",
     "\n",
     "# create the figure and a list of axes\n",
     "fig, axis = plt.subplots(figsize=(34, 14))\n",
@@ -782,7 +800,7 @@
     "\n",
     "# add labels, titles, ticks, etc.\n",
     "axis.set_ylabel(\"Percentage of predictions\")\n",
-    "axis.set_xlim(1, 23 + 0.1)\n",
+    "axis.set_xlim(1, 25 + 0.1)\n",
     "axis.set_ylim(0, 125)\n",
     "\n",
     "assert len(dockgen_rmsd_lt_2_bar) == len(dockgen_rmsd_lt_2_and_pb_valid_bar), (\n",
@@ -845,7 +863,7 @@
     "axis.grid(axis=\"y\", color=\"#EAEFF8\")\n",
     "axis.set_axisbelow(True)\n",
     "\n",
-    "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 13, 14, 16, 18, 20, 22])\n",
+    "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 14, 14 + 1e-3, 16, 18, 20, 22, 24])\n",
     "axis.set_xticks([1 + 0.1], minor=True)\n",
     "axis.set_xticklabels(\n",
     "    [\n",
@@ -856,10 +874,11 @@
     "        \"RoseTTAFold-AA\",\n",
     "        \"AF3-Single-Seq\",\n",
     "        \"Chai-1-Single-Seq\",\n",
-    "        \"DL-based blind\",\n",
     "        \"NeuralPLexer\",\n",
+    "        \"DL-based blind\",\n",
     "        \"FlowDock-HP\",\n",
     "        \"FlowDock-AFT\",\n",
+    "        \"FlowDock-PFT\",\n",
     "        \"FlowDock-ESMFold\",\n",
     "        \"FlowDock-AF3\",\n",
     "    ]\n",
@@ -873,7 +892,7 @@
     "axis.tick_params(axis=\"y\", which=\"major\", left=\"off\", right=\"on\", color=\"#EAEFF8\")\n",
     "\n",
     "# vertical alignment of xtick labels\n",
-    "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+    "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
     "for tick, y in zip(axis.get_xticklabels(), vert_alignments):\n",
     "    tick.set_y(y)\n",
     "\n",
@@ -918,7 +937,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json b/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json
new file mode 100644
index 0000000..062491f
--- /dev/null
+++ b/notebooks/posebusters_benchmark_mol_to_protein_mapping_1.json
@@ -0,0 +1 @@
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diff --git a/notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json b/notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json
new file mode 100644
index 0000000..062491f
--- /dev/null
+++ b/notebooks/posebusters_benchmark_mol_to_protein_mapping_2.json
@@ -0,0 +1 @@
+{"7PK0_BYC": "Q2KIR7", "7ES1_UDP": "Q0DPB7", "7NUT_GLP": "Q9Y303", "7NF0_BYN": "G9NLP8", "7U0U_FK5": "Q4WLV6", "7D6O_MTE": "Unknown", "6VTA_AKN": "Q52424", "7L7C_XQ1": "P0AEG4", "7ZF0_DHR": "Q9SBL1", "7V43_C4O": "A0A088FL33", "6XBO_5MC": "Q61420", "7U3J_L6U": "Q8IVV7", "7OZC_G6S": "Q8A346", "7P1M_4IU": "O00214", "5SIS_JSM": "Q9Y233", "7MFP_Z7P": "Q5UPL1", "7JY3_VUD": "P68871", "7XI7_4RI": "P00374", "7VWF_K55": "Q03181", "7FHA_ADX": "O95340", "7TBU_S3P": "Q5AME2", "7R6J_2I7": "G0SFD1", "6YR2_T1C": "P0ACT4", "7L00_XCJ": "A0A031WIF2", "7VC5_9SF": "A0A7J6JUK2", "7Z7F_IF3": "Q9Y5A9", "7TSF_H4B": "P29476", "7SZA_DUI": "Q96S44", "7XJN_NSD": "Q9KS37", "7BCP_GCO": "R4JTF7", "7QTA_URI": "P24021", "8EAB_VN2": "P26718", "7PT3_3KK": "P0DUV9", "8AIE_M7L": "D5EHC5", "7THI_PGA": "P07738", "8SLG_G5A": "G7CIG9", "8AAU_LH0": "P53667", "7KQU_YOF": "Unknown", "7LCU_XTA": "P09958", "7LT0_ONJ": "P07900", "7V3S_5I9": "P08581", "7RSV_7IQ": "Q8NEB9", "7XPO_UPG": "C0HI30", "5SD5_HWI": "P9WNX1", "7QPP_VDX": 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diff --git a/notebooks/posebusters_benchmark_mol_to_protein_mapping_3.json b/notebooks/posebusters_benchmark_mol_to_protein_mapping_3.json
new file mode 100644
index 0000000..062491f
--- /dev/null
+++ b/notebooks/posebusters_benchmark_mol_to_protein_mapping_3.json
@@ -0,0 +1 @@
+{"7PK0_BYC": "Q2KIR7", "7ES1_UDP": "Q0DPB7", "7NUT_GLP": "Q9Y303", "7NF0_BYN": "G9NLP8", "7U0U_FK5": "Q4WLV6", "7D6O_MTE": "Unknown", "6VTA_AKN": "Q52424", "7L7C_XQ1": "P0AEG4", "7ZF0_DHR": "Q9SBL1", "7V43_C4O": "A0A088FL33", "6XBO_5MC": "Q61420", "7U3J_L6U": "Q8IVV7", "7OZC_G6S": "Q8A346", "7P1M_4IU": "O00214", "5SIS_JSM": "Q9Y233", "7MFP_Z7P": "Q5UPL1", "7JY3_VUD": "P68871", "7XI7_4RI": "P00374", "7VWF_K55": "Q03181", "7FHA_ADX": "O95340", "7TBU_S3P": "Q5AME2", "7R6J_2I7": "G0SFD1", "6YR2_T1C": "P0ACT4", "7L00_XCJ": "A0A031WIF2", "7VC5_9SF": "A0A7J6JUK2", "7Z7F_IF3": "Q9Y5A9", "7TSF_H4B": "P29476", "7SZA_DUI": "Q96S44", "7XJN_NSD": "Q9KS37", "7BCP_GCO": "R4JTF7", "7QTA_URI": "P24021", "8EAB_VN2": "P26718", "7PT3_3KK": "P0DUV9", "8AIE_M7L": "D5EHC5", "7THI_PGA": "P07738", "8SLG_G5A": "G7CIG9", "8AAU_LH0": "P53667", "7KQU_YOF": "Unknown", "7LCU_XTA": "P09958", "7LT0_ONJ": "P07900", "7V3S_5I9": "P08581", "7RSV_7IQ": "Q8NEB9", "7XPO_UPG": "C0HI30", "5SD5_HWI": "P9WNX1", "7QPP_VDX": 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diff --git a/notebooks/posebusters_benchmark_protein_grouped_mols_1.json b/notebooks/posebusters_benchmark_protein_grouped_mols_1.json
new file mode 100644
index 0000000..0f8ed1b
--- /dev/null
+++ b/notebooks/posebusters_benchmark_protein_grouped_mols_1.json
@@ -0,0 +1 @@
+{"Q2KIR7": ["7PK0_BYC"], "Q0DPB7": ["7ES1_UDP"], "Q9Y303": ["7NUT_GLP"], "G9NLP8": ["7NF0_BYN"], "Q4WLV6": ["7U0U_FK5"], "Q52424": ["6VTA_AKN"], "P0AEG4": ["7L7C_XQ1"], "Q9SBL1": ["7ZF0_DHR"], "A0A088FL33": ["7V43_C4O"], "Q61420": ["6XBO_5MC"], "Q8IVV7": ["7U3J_L6U"], "Q8A346": ["7OZC_G6S"], "O00214": ["7P1M_4IU"], "Q9Y233": ["5SIS_JSM"], "Q5UPL1": ["7MFP_Z7P"], "P68871": ["7JY3_VUD"], "P00374": ["7XI7_4RI"], "Q03181": ["7VWF_K55"], "O95340": ["7FHA_ADX"], "Q5AME2": ["7TBU_S3P"], "G0SFD1": ["7R6J_2I7"], "P0ACT4": ["6YR2_T1C"], "A0A031WIF2": ["7L00_XCJ"], "A0A7J6JUK2": ["7VC5_9SF"], "Q9Y5A9": ["7Z7F_IF3"], "P29476": ["7TSF_H4B"], "Q96S44": ["7SZA_DUI"], "Q9KS37": ["7XJN_NSD"], "R4JTF7": ["7BCP_GCO"], "P24021": ["7QTA_URI"], "P26718": ["8EAB_VN2"], "P0DUV9": ["7PT3_3KK"], "D5EHC5": ["8AIE_M7L"], "P07738": ["7THI_PGA"], "G7CIG9": ["8SLG_G5A"], "P53667": ["8AAU_LH0"], "P09958": ["7LCU_XTA"], "P07900": ["7LT0_ONJ"], "P08581": ["7V3S_5I9"], "Q8NEB9": ["7RSV_7IQ"], "C0HI30": ["7XPO_UPG"], 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diff --git a/notebooks/posebusters_benchmark_protein_grouped_mols_2.json b/notebooks/posebusters_benchmark_protein_grouped_mols_2.json
new file mode 100644
index 0000000..0f8ed1b
--- /dev/null
+++ b/notebooks/posebusters_benchmark_protein_grouped_mols_2.json
@@ -0,0 +1 @@
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diff --git a/notebooks/posebusters_benchmark_protein_grouped_mols_3.json b/notebooks/posebusters_benchmark_protein_grouped_mols_3.json
new file mode 100644
index 0000000..0f8ed1b
--- /dev/null
+++ b/notebooks/posebusters_benchmark_protein_grouped_mols_3.json
@@ -0,0 +1 @@
+{"Q2KIR7": ["7PK0_BYC"], "Q0DPB7": ["7ES1_UDP"], "Q9Y303": ["7NUT_GLP"], "G9NLP8": ["7NF0_BYN"], "Q4WLV6": ["7U0U_FK5"], "Q52424": ["6VTA_AKN"], "P0AEG4": ["7L7C_XQ1"], "Q9SBL1": ["7ZF0_DHR"], "A0A088FL33": ["7V43_C4O"], "Q61420": ["6XBO_5MC"], "Q8IVV7": ["7U3J_L6U"], "Q8A346": ["7OZC_G6S"], "O00214": ["7P1M_4IU"], "Q9Y233": ["5SIS_JSM"], "Q5UPL1": ["7MFP_Z7P"], "P68871": ["7JY3_VUD"], "P00374": ["7XI7_4RI"], "Q03181": ["7VWF_K55"], "O95340": ["7FHA_ADX"], "Q5AME2": ["7TBU_S3P"], "G0SFD1": ["7R6J_2I7"], "P0ACT4": ["6YR2_T1C"], "A0A031WIF2": ["7L00_XCJ"], "A0A7J6JUK2": ["7VC5_9SF"], "Q9Y5A9": ["7Z7F_IF3"], "P29476": ["7TSF_H4B"], "Q96S44": ["7SZA_DUI"], "Q9KS37": ["7XJN_NSD"], "R4JTF7": ["7BCP_GCO"], "P24021": ["7QTA_URI"], "P26718": ["8EAB_VN2"], "P0DUV9": ["7PT3_3KK"], "D5EHC5": ["8AIE_M7L"], "P07738": ["7THI_PGA"], "G7CIG9": ["8SLG_G5A"], "P53667": ["8AAU_LH0"], "P09958": ["7LCU_XTA"], "P07900": ["7LT0_ONJ"], "P08581": ["7V3S_5I9"], "Q8NEB9": ["7RSV_7IQ"], "C0HI30": ["7XPO_UPG"], "P9WNX1": ["5SD5_HWI"], "P11473": ["7QPP_VDX"], "P11309": ["7QFM_AY3"], "P14779": ["7WY1_D0L"], "F7X6I3": ["8DKO_TFB"], "A0A0G4DBU7": ["7CIJ_G0C"], "B1Q4V2": ["7W05_GMP"], "P12821": ["7Q27_8KC"], "P01009": ["7NPL_UKZ"], "Q8N371": ["7UQ3_O2U"], "Q84G19": ["7VYJ_CA0"], "P0AG05": ["7UY4_SMI"], "P0AEK4": ["7UMW_NAD"], "P43490": ["8DSC_NCA"], "Q5TCY1": ["7JXX_VP7", "7ZHP_IQY"], "P53597": ["7MSR_DCA"], "Q96MU7": ["7PJQ_OWH"], "O80992": ["7MWN_WI5"], "P43133": ["6YMS_OZH"], "P08337": ["7X9K_8OG"], "P17931": ["7XFA_D9J", "7RH3_59O"], "G7CMG5": ["8G6P_API"], "P53686": ["7F51_BA7"], "O96935": ["8EYE_X4I"], "B6T563": ["6ZK5_IMH"], "P20701": ["7KC5_BJZ"], "Q58036": ["7TOM_5AD"], "P61964": ["7UAS_MBU"], "P74102": ["7ZXV_45D"], "P0AES6": ["7DQL_4CL"], "P56817": ["7MYU_ZR7"], "Q9WXT1": ["7ZDY_6MJ"], "Q79FX8": ["7Q2B_M6H"], "P53671": ["7QHG_T3B"], "Q9UGN5": ["7R59_I5F"], "Q8TPS4": ["7VQ9_ISY"], "O43813": ["8D19_GSH"], "A0A0H3B558": ["7W06_ITN"], "P01116": ["7SCW_GSP"], "A0A2P2GK84": ["7XQZ_FPF"], 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["7AKL_RK5"], "A0A7U3TBV3": ["7ODY_DGI"], "A0A8I3AZS8": ["6ZPB_3D1"], "Q9WZY4": ["7KB1_WBJ"], "Q9WZL1": ["7CTM_BDP"], "Q12830": ["7F5D_EUO"], "A0A0H3GV01": ["7TM6_GPJ"], "T2D1F5": ["7ECR_SIN"], "Q59470": ["7XRL_FWK"], "Q5JGJ6": ["7CNS_PMV"], "P18177": ["7LOU_IFM"], "P00915": ["7ZL5_IWE"], "O60674": ["6WTN_RXT"], "J9VFT1": ["8G0V_YHT"], "A0A1R3S1W7": ["7WUY_76N"], "P72683": ["7MY1_IPE"], "P9WMC1": ["7NGW_UAW"], "P34897": ["8AQL_PLG"], "Q92769": ["7ZZW_KKW"], "Q9L1K2": ["7MGY_ZD1"], "P49077": ["6YSP_PAL"], "O00560": ["7FT9_4MB"], "Q9H8M2": ["6YQW_82I"], "P22629": ["7NLV_UJE"], "Q6XEC0": ["7JHQ_VAJ"], "Q1R0W5": ["7NSW_HC4"], "A0A0B0SG80": ["7OMX_CNA"], "Q1CVJ4": ["7O0N_CDP"], "A0QZY0": ["8HFN_XGC"], "Q9Z2X8": ["7OFF_VCB"], "Q92997": ["6ZC3_JOR"], "P20248": ["7QHL_D5P"], "Q0CMX0": ["7P1F_KFN"], "A0A5S9CYM0": ["8AP0_PRP"], "A0A133CTP6": ["7TB0_UD1"], "A5K1A2": ["6TW5_9M2"], "P00489": ["7Q5I_I0F"], "A0A0F6AY66": ["7N4W_P4V"], "O94804": ["7QGP_DJ8"], "B1MHW4": ["6Z0R_Q4H"], "P29317": ["8BOM_QU6"], "A0A248QE08": ["6YRV_PJ8"], "Q9UZY0": ["7OLI_8HG"], "O00255": ["7UJ4_OQ4"], "Q46085": ["7ZOC_T8E"], "Q4Q5S8": ["6TW7_NZB"], "P10276": ["7WQQ_5Z6"], "P00533": ["8A2D_KXY"], "Q96PN6": ["8CNH_V6U"], "A0A3Q0KSG2": ["7PRI_7TI"], "Q02127": ["6M2B_EZO", "8DHG_T78"], "Q0C8G1": ["7XG5_PLP"], "Q2G1C0": ["6XHT_V2V"], "A0A0H2ZQB9": ["7TXK_LW8"], "P0DW58": ["7UAW_MF6"], "P9WNS3": ["7A9H_TPP"], "Q5SLG6": ["7TH4_FFO"], "K2K6A3": ["7UJ5_DGL"], "A0A2S0PEL5": ["7RC3_SAH"], "P00327": ["7UTW_NAI"], "A0QVP7": ["7ED2_A3P"], "A0A0S4TLR1": ["7P2I_MFU"], "Q6U8B0": ["8CSD_C5P"], "P54577": ["7ROU_66I"], "Q9ZVN4": ["7MOI_HPS"], "P0ABQ4": ["6XG5_TOP"], "B3PEE6": ["7P4C_5OV"], "O49003": ["7PGX_FMN"], "Q08467": ["6Z1C_7EY"], "P00791": ["6XCT_478"], "P29475": ["7TS6_KMI"], "P28720": ["7A9E_R4W"], "Q8CVD0": ["6T88_MWQ"], "A0A3Q7HTY9": ["7Z1Q_NIO"], "Q15562": ["7TYP_KUR"], "P25321": ["7PIH_7QW"], "Q82Y41": ["7C3U_AZG"], "A7RNF3": ["7N7H_CTP"], "Q89Y83": ["7LJN_GTP"], "A0A175WC91": ["7JMV_4NC"], "Q05397": ["6YT6_PKE"], "A0A067E666": ["8AY3_OE3"], "Q15119": ["7VBU_6I4"], "O14757": ["7BJJ_TVW"], "Q92918": ["7R9N_F97", "7SIU_9ID"], "Q16654": ["7EBG_J0L"], "J8G6Z1": ["7UXS_OJC"], "Q9Y4B6": ["8F8E_XJI"], "P0CJ63": ["7L5F_XNG"], "Q9K2N0": ["7AFX_R9K"], "P10275": ["7ZU2_DHT"], "O74036": ["8C3N_ADP"], "C5ZW02": ["7N7B_T3F"], "P0A1E3": ["6Z4N_Q7B"], "O14965": ["7ZTL_BCN"], "P54687": ["7NXO_UU8"], "P68400": ["8AEM_LVF"], "B4RNT1": ["7NU0_DCL"], "A6YEH8": ["7XBV_APC"], "A0A9Y2YA89": ["7V3N_AKG"], "Q8IAR7": ["7ROR_69X"], "Q8ZNC4": ["8D5D_5DK"], "Q9HVZ9": ["8DP2_UMA"], "P25440": ["7USH_82V"], "P80188": ["6Z2C_Q5E"], "P25774": ["6YYO_Q1K"], "P05326": ["6ZAE_ACV"], "F6BL85": ["7DKT_GLF"], "P05413": ["7WPW_F15"], "Q5ZRR1": ["7SDD_4IP"], "Q5ZSZ6": ["7WCF_ACP"], "Q9R9V9": ["8AUH_L9I"]}
diff --git a/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb b/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb
new file mode 100644
index 0000000..47dcda3
--- /dev/null
+++ b/notebooks/posebusters_benchmark_structure_prediction_chemical_similarity_analysis.ipynb
@@ -0,0 +1,723 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## PoseBusters Benchmark Structure Prediction Chemical Similarity Analysis"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Import packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "import json\n",
+    "import os\n",
+    "from collections import defaultdict\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import requests\n",
+    "import seaborn as sns\n",
+    "from rdkit import Chem\n",
+    "from rdkit.Chem import DataStructs\n",
+    "from tqdm import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Configure packages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pd.options.mode.copy_on_write = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define constants"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# General variables\n",
+    "baseline_methods = [\n",
+    "    # \"vina_p2rank\",\n",
+    "    # \"diffdock\",\n",
+    "    # \"dynamicbind\",\n",
+    "    # \"rfaa\",\n",
+    "    # \"alphafold3\",\n",
+    "    # \"chai-lab\",\n",
+    "    # \"neuralplexer\",\n",
+    "    # \"flowdock_hp\",\n",
+    "    # \"flowdock_aft\",\n",
+    "    # \"flowdock_esmfold\",\n",
+    "    \"flowdock\",\n",
+    "]\n",
+    "max_num_repeats_per_method = 3\n",
+    "\n",
+    "# Filepaths for each baseline method\n",
+    "globals()[\"vina_output_dir\"] = os.path.join(\"..\", \"forks\", \"Vina\", \"inference\")\n",
+    "globals()[\"diffdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"DiffDock\", \"inference\")\n",
+    "globals()[\"dynamicbind_output_dir\"] = os.path.join(\n",
+    "    \"..\", \"forks\", \"DynamicBind\", \"inference\", \"outputs\", \"results\"\n",
+    ")\n",
+    "globals()[\"rfaa_output_dir\"] = os.path.join(\"..\", \"forks\", \"RoseTTAFold-All-Atom\", \"inference\")\n",
+    "globals()[\"alphafold3_output_dir\"] = os.path.join(\"..\", \"forks\", \"alphafold3\", \"inference\")\n",
+    "globals()[\"chai-lab_output_dir\"] = os.path.join(\"..\", \"forks\", \"chai-lab\", \"inference\")\n",
+    "globals()[\"neuralplexer_output_dir\"] = os.path.join(\"..\", \"forks\", \"NeuralPLexer\", \"inference\")\n",
+    "globals()[\"flowdock_hp_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"hp_inference\")\n",
+    "globals()[\"flowdock_aft_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"aft_inference\")\n",
+    "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n",
+    "    \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n",
+    ")\n",
+    "globals()[\"flowdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"inference\")\n",
+    "\n",
+    "for repeat_index in range(1, max_num_repeats_per_method + 1):\n",
+    "    # P2Rank-Vina results\n",
+    "    globals()[f\"vina_p2rank_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"vina_output_dir\"],\n",
+    "        f\"vina_p2rank_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"vina_p2rank_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"vina_output_dir\"],\n",
+    "            f\"vina_p2rank_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # DiffDock results\n",
+    "    globals()[f\"diffdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"diffdock_output_dir\"],\n",
+    "        f\"diffdock_posebusters_benchmark_output_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"diffdock_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"diffdock_output_dir\"],\n",
+    "            f\"diffdock_posebusters_benchmark_output_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # DynamicBind results\n",
+    "    globals()[f\"dynamicbind_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"dynamicbind_output_dir\"],\n",
+    "        f\"posebusters_benchmark_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"dynamicbind_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"dynamicbind_output_dir\"],\n",
+    "            f\"posebusters_benchmark_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # RoseTTAFold-All-Atom results\n",
+    "    globals()[f\"rfaa_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"rfaa_output_dir\"],\n",
+    "        f\"rfaa_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"rfaa_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"rfaa_output_dir\"],\n",
+    "        f\"rfaa_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "\n",
+    "    # AlphaFold 3 (Single-Seq) results\n",
+    "    globals()[f\"alphafold3_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"alphafold3_output_dir\"],\n",
+    "        f\"alphafold3_ss_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"alphafold3_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"alphafold3_output_dir\"],\n",
+    "            f\"alphafold3_ss_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Chai-1 (Single-Seq) results\n",
+    "    globals()[f\"chai-lab_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"chai-lab_output_dir\"],\n",
+    "        f\"chai-lab_ss_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"chai-lab_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"chai-lab_output_dir\"],\n",
+    "            f\"chai-lab_ss_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # NeuralPLexer results\n",
+    "    globals()[f\"neuralplexer_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"neuralplexer_output_dir\"],\n",
+    "        f\"neuralplexer_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"neuralplexer_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"neuralplexer_output_dir\"],\n",
+    "            f\"neuralplexer_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # FlowDock-HP results\n",
+    "    globals()[f\"flowdock_hp_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"flowdock_hp_output_dir\"],\n",
+    "        f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"flowdock_hp_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_hp_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # FlowDock-AFT results\n",
+    "    globals()[f\"flowdock_aft_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"flowdock_aft_output_dir\"],\n",
+    "        f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"flowdock_aft_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_aft_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # FlowDock-ESMFold results\n",
+    "    globals()[f\"flowdock_esmfold_posebusters_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_esmfold_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "    globals()[f\"flowdock_esmfold_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_esmfold_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # FlowDock results\n",
+    "    globals()[f\"flowdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
+    "        globals()[\"flowdock_output_dir\"],\n",
+    "        f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "        \"bust_results.csv\",\n",
+    "    )\n",
+    "    globals()[f\"flowdock_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "# Mappings\n",
+    "method_mapping = {\n",
+    "    \"vina_p2rank\": \"P2Rank-Vina\",\n",
+    "    \"diffdock\": \"DiffDock-L\",\n",
+    "    \"dynamicbind\": \"DynamicBind\",\n",
+    "    \"rfaa\": \"RoseTTAFold-AA\",\n",
+    "    \"alphafold3\": \"AF3-Single-Seq\",\n",
+    "    \"chai-lab\": \"Chai-1-Single-Seq\",\n",
+    "    \"neuralplexer\": \"NeuralPLexer\",\n",
+    "    \"flowdock_hp\": \"FlowDock-HP\",\n",
+    "    \"flowdock_aft\": \"FlowDock-AFT\",\n",
+    "    \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n",
+    "    \"flowdock\": \"FlowDock-AF3\",\n",
+    "}\n",
+    "\n",
+    "method_category_mapping = {\n",
+    "    \"vina_p2rank\": \"Conventional blind\",\n",
+    "    \"diffdock\": \"DL-based blind\",\n",
+    "    \"dynamicbind\": \"DL-based blind\",\n",
+    "    \"rfaa\": \"DL-based blind\",\n",
+    "    \"alphafold3\": \"DL-based blind\",\n",
+    "    \"chai-lab\": \"DL-based blind\",\n",
+    "    \"neuralplexer\": \"DL-based blind\",\n",
+    "    \"flowdock_hp\": \"DL-based blind\",\n",
+    "    \"flowdock_aft\": \"DL-based blind\",\n",
+    "    \"flowdock_esmfold\": \"DL-based blind\",\n",
+    "    \"flowdock\": \"DL-based blind\",\n",
+    "}\n",
+    "\n",
+    "# Metrics\n",
+    "BUST_TEST_COLUMNS = [\n",
+    "    # accuracy #\n",
+    "    \"rmsd_≤_2å\",\n",
+    "    # chemical validity and consistency #\n",
+    "    \"mol_pred_loaded\",\n",
+    "    \"mol_true_loaded\",\n",
+    "    \"mol_cond_loaded\",\n",
+    "    \"sanitization\",\n",
+    "    \"molecular_formula\",\n",
+    "    \"molecular_bonds\",\n",
+    "    \"tetrahedral_chirality\",\n",
+    "    \"double_bond_stereochemistry\",\n",
+    "    # intramolecular validity #\n",
+    "    \"bond_lengths\",\n",
+    "    \"bond_angles\",\n",
+    "    \"internal_steric_clash\",\n",
+    "    \"aromatic_ring_flatness\",\n",
+    "    \"double_bond_flatness\",\n",
+    "    \"internal_energy\",\n",
+    "    # intermolecular validity #\n",
+    "    \"minimum_distance_to_protein\",\n",
+    "    \"minimum_distance_to_organic_cofactors\",\n",
+    "    \"minimum_distance_to_inorganic_cofactors\",\n",
+    "    \"volume_overlap_with_protein\",\n",
+    "    \"volume_overlap_with_organic_cofactors\",\n",
+    "    \"volume_overlap_with_inorganic_cofactors\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Report test results for each baseline method"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load and report test results for each baseline method\n",
+    "for config in [\"\", \"_relaxed\"]:\n",
+    "    for method in baseline_methods:\n",
+    "        for repeat_index in range(1, max_num_repeats_per_method + 1):\n",
+    "            method_title = method_mapping[method]\n",
+    "\n",
+    "            if not os.path.exists(\n",
+    "                globals()[f\"{method}_posebusters{config}_bust_results_csv_filepath_{repeat_index}\"]\n",
+    "            ):\n",
+    "                continue\n",
+    "\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"] = pd.read_csv(\n",
+    "                globals()[f\"{method}_posebusters{config}_bust_results_csv_filepath_{repeat_index}\"]\n",
+    "            )\n",
+    "            assert (\n",
+    "                \"mol_id\"\n",
+    "                in globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"].columns\n",
+    "            ), f\"mol_id column not found in {method_title}{config} bust_results CSV file for repeat {repeat_index}\"\n",
+    "            globals()[\n",
+    "                f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"\n",
+    "            ] = globals()[f\"{method}_posebusters{config}_bust_results_{repeat_index}\"][\n",
+    "                BUST_TEST_COLUMNS + [\"rmsd\", \"mol_id\", \"inchi_crystal\"]\n",
+    "            ]\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n",
+    "                :, \"pb_valid\"\n",
+    "            ] = (\n",
+    "                globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"]\n",
+    "                .iloc[:, 1:-3]\n",
+    "                .all(axis=1)\n",
+    "            )\n",
+    "\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n",
+    "                :, \"method\"\n",
+    "            ] = method\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n",
+    "                :, \"post-processing\"\n",
+    "            ] = (\"energy minimization\" if config == \"_relaxed\" else \"none\")\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n",
+    "                :, \"dataset\"\n",
+    "            ] = \"posebusters\"\n",
+    "            globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"].loc[\n",
+    "                :, \"docked_ligand_successfully_loaded\"\n",
+    "            ] = globals()[f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"][\n",
+    "                [\"mol_pred_loaded\", \"mol_true_loaded\", \"mol_cond_loaded\"]\n",
+    "            ].all(\n",
+    "                axis=1\n",
+    "            )\n",
+    "\n",
+    "            globals()[f\"{method}{config}_bust_results_table_{repeat_index}\"] = globals()[\n",
+    "                f\"{method}_posebusters{config}_bust_results_table_{repeat_index}\"\n",
+    "            ]\n",
+    "\n",
+    "            print(\n",
+    "                f\"{method_title}{config}_{repeat_index} PoseBusters Benchmark set `rmsd_≤_2å`: {globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}']['rmsd_≤_2å'].mean()}\"\n",
+    "            )\n",
+    "            print(\n",
+    "                f\"{method_title}{config}_{repeat_index} PoseBusters Benchmark set `rmsd_≤_2å and pb_valid`: {globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}'][globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}']['pb_valid']]['rmsd_≤_2å'].sum() / len(globals()[f'{method}_posebusters{config}_bust_results_table_{repeat_index}'])}\"\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Define helper functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def assign_method_index(method: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Assign method index for plotting.\n",
+    "\n",
+    "    :param method: Method name.\n",
+    "    :return: Method index.\n",
+    "    \"\"\"\n",
+    "    return list(method_mapping.keys()).index(method)\n",
+    "\n",
+    "\n",
+    "def categorize_method(method: str) -> str:\n",
+    "    \"\"\"\n",
+    "    Categorize method for plotting.\n",
+    "\n",
+    "    :param method: Method name.\n",
+    "    :return: Method category.\n",
+    "    \"\"\"\n",
+    "    return method_category_mapping.get(method, \"Misc\")\n",
+    "\n",
+    "\n",
+    "def get_uniprot_mapping(pdb_id: str) -> set:\n",
+    "    \"\"\"\n",
+    "    Get the UniProt mapping for a given PDB ID using the PDBe API.\n",
+    "\n",
+    "    :param pdb_id: PDB ID (e.g., \"1A2B\").\n",
+    "    :return: Set of UniProt IDs mapped to the PDB ID, or None if not found.\n",
+    "    \"\"\"\n",
+    "    # use the PDBe API to get the mapping data\n",
+    "    url = f\"https://www.ebi.ac.uk/pdbe/api/mappings/uniprot/{pdb_id.lower()}\"\n",
+    "    response = requests.get(url)\n",
+    "    if response.status_code != 200:\n",
+    "        return None\n",
+    "    data = response.json()\n",
+    "    uniprot_ids = set()\n",
+    "    if pdb_id.lower() in data:\n",
+    "        # NOTE: each mapping corresponds to one chain/segment mapped to a UniProt entry\n",
+    "        for uniprot_id in data[pdb_id.lower()].get(\"UniProt\", []):\n",
+    "            uniprot_ids.add(uniprot_id)\n",
+    "    return uniprot_ids\n",
+    "\n",
+    "\n",
+    "def group_has_low_similarity(group: pd.DataFrame, threshold: float = 0.6) -> bool:\n",
+    "    \"\"\"\n",
+    "    Return True if every pair of molecules in the group has Tanimoto similarity less than threshold.\n",
+    "    Groups with only one ligand are considered valid.\n",
+    "\n",
+    "    :param group: DataFrame group containing the fingerprints.\n",
+    "    :param threshold: Similarity threshold for filtering.\n",
+    "    :return: True if the group has low similarity, False otherwise.\n",
+    "    \"\"\"\n",
+    "    max_sim = 0.0\n",
+    "    fps = group[\"fp\"].tolist()\n",
+    "    if len(fps) < 2:\n",
+    "        group[\"max_fp_sim\"] = max_sim\n",
+    "        return True\n",
+    "    for fp1, fp2 in itertools.combinations(fps, 2):\n",
+    "        # Only calculate similarity if both fingerprints are valid.\n",
+    "        if fp1 is not None and fp2 is not None:\n",
+    "            sim = DataStructs.FingerprintSimilarity(fp1, fp2)\n",
+    "            max_sim = max(max_sim, sim)\n",
+    "            if sim >= threshold:\n",
+    "                return False\n",
+    "    group[\"max_fp_sim\"] = max_sim\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Standardize metrics"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load and organize the PoseBusters Benchmark results CSVs\n",
+    "for repeat_index in range(1, max_num_repeats_per_method + 1):\n",
+    "    globals()[f\"results_table_{repeat_index}\"] = pd.concat(\n",
+    "        [\n",
+    "            globals()[f\"{method}{config}_bust_results_table_{repeat_index}\"]\n",
+    "            for method in baseline_methods\n",
+    "            for config in [\"\", \"_relaxed\"]\n",
+    "            if f\"{method}{config}_bust_results_table_{repeat_index}\" in globals()\n",
+    "        ]\n",
+    "    )\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"method_category\"] = globals()[\n",
+    "        f\"results_table_{repeat_index}\"\n",
+    "    ][\"method\"].apply(categorize_method)\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"method_assignment_index\"] = globals()[\n",
+    "        f\"results_table_{repeat_index}\"\n",
+    "    ][\"method\"].apply(assign_method_index)\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_≤_2å\"].fillna(False)\n",
+    "    )\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold_and_pb_valid\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"].loc[:, \"rmsd_within_threshold\"]\n",
+    "    ) & (globals()[f\"results_table_{repeat_index}\"].loc[:, \"pb_valid\"].fillna(False))\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"RMSD ≤ 2 Å & PB-Valid\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"]\n",
+    "        .loc[:, \"rmsd_within_threshold_and_pb_valid\"]\n",
+    "        .astype(int)\n",
+    "    )\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"RMSD ≤ 2 Å\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"]\n",
+    "        .loc[:, \"rmsd_within_threshold\"]\n",
+    "        .fillna(False)\n",
+    "        .astype(int)\n",
+    "    )\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"dataset\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"]\n",
+    "        .loc[:, \"dataset\"]\n",
+    "        .map({\"posebusters\": \"PoseBusters Benchmark set\"})\n",
+    "    )\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"method\"] = (\n",
+    "        globals()[f\"results_table_{repeat_index}\"].loc[:, \"method\"].map(method_mapping)\n",
+    "    )\n",
+    "\n",
+    "    # group ligands according to their binding proteins (here defined as their first UniProt IDs, if available)\n",
+    "    mol_to_protein_mapping = dict()\n",
+    "    protein_grouped_mols = defaultdict(list)\n",
+    "    mol_ids = list(set(globals()[f\"results_table_{repeat_index}\"].mol_id))\n",
+    "\n",
+    "    protein_grouped_mols_json_filepath = (\n",
+    "        f\"posebusters_benchmark_protein_grouped_mols_{repeat_index}.json\"\n",
+    "    )\n",
+    "    mol_to_protein_mapping_json_filepath = (\n",
+    "        f\"posebusters_benchmark_mol_to_protein_mapping_{repeat_index}.json\"\n",
+    "    )\n",
+    "\n",
+    "    if os.path.exists(protein_grouped_mols_json_filepath):\n",
+    "        # load cached groupings if available\n",
+    "        with open(protein_grouped_mols_json_filepath, \"r\") as f:\n",
+    "            protein_grouped_mols = json.load(f)\n",
+    "    if os.path.exists(mol_to_protein_mapping_json_filepath):\n",
+    "        # load cached mappings if available\n",
+    "        with open(mol_to_protein_mapping_json_filepath, \"r\") as f:\n",
+    "            mol_to_protein_mapping = json.load(f)\n",
+    "\n",
+    "    if not (protein_grouped_mols and mol_to_protein_mapping):\n",
+    "        for mol in tqdm(mol_ids, desc=\"Grouping ligands by protein\"):\n",
+    "            pdb_id = mol.split(\"_\")[0]\n",
+    "            uniprot_ids = get_uniprot_mapping(pdb_id)\n",
+    "\n",
+    "            group_uniprot_id = None\n",
+    "\n",
+    "            if uniprot_ids:\n",
+    "                for uniprot_id in uniprot_ids:\n",
+    "                    if uniprot_id in protein_grouped_mols:\n",
+    "                        group_uniprot_id = uniprot_id\n",
+    "                        protein_grouped_mols[group_uniprot_id].append(mol)\n",
+    "                        break\n",
+    "\n",
+    "                if not group_uniprot_id:\n",
+    "                    group_uniprot_id = list(sorted(uniprot_ids))[0]\n",
+    "                    protein_grouped_mols[group_uniprot_id] = [mol]\n",
+    "            else:\n",
+    "                group_uniprot_id = \"Unknown\"\n",
+    "\n",
+    "            mol_to_protein_mapping[mol] = group_uniprot_id\n",
+    "\n",
+    "        # cache groupings\n",
+    "        with open(protein_grouped_mols_json_filepath, \"w\") as f:\n",
+    "            json.dump(protein_grouped_mols, f)\n",
+    "        with open(mol_to_protein_mapping_json_filepath, \"w\") as f:\n",
+    "            json.dump(mol_to_protein_mapping, f)\n",
+    "\n",
+    "    # add the protein group IDs to the results table\n",
+    "    globals()[f\"results_table_{repeat_index}\"].loc[:, \"protein_id\"] = globals()[\n",
+    "        f\"results_table_{repeat_index}\"\n",
+    "    ][\"mol_id\"].map(mol_to_protein_mapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Make plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# RMSD Violin Plot of PoseBusters Benchmark Set Results (Relaxed vs. Unrelaxed and Grouped Per Protein) #\n",
+    "\n",
+    "# prepare data for the violin plots to plot\n",
+    "colors = [\"#FB8072\", \"#BEBADA\"]\n",
+    "\n",
+    "# combine results for each dataset across all three repeats\n",
+    "combined_data_list = []\n",
+    "for repeat_index in range(1, max_num_repeats_per_method + 1):\n",
+    "    ad_relaxed_results_table = globals()[f\"results_table_{repeat_index}\"][\n",
+    "        (globals()[f\"results_table_{repeat_index}\"][\"dataset\"] == \"PoseBusters Benchmark set\")\n",
+    "        & (globals()[f\"results_table_{repeat_index}\"][\"post-processing\"] == \"energy minimization\")\n",
+    "        & (globals()[f\"results_table_{repeat_index}\"][\"protein_id\"] != \"Unknown\")\n",
+    "    ]\n",
+    "    ad_relaxed_results_table.loc[\n",
+    "        :, \"protein_group_size\"\n",
+    "    ] = ad_relaxed_results_table.protein_id.value_counts()[\n",
+    "        ad_relaxed_results_table.protein_id\n",
+    "    ].values\n",
+    "    ad_relaxed_results_table = ad_relaxed_results_table[\n",
+    "        ad_relaxed_results_table.protein_group_size > 1\n",
+    "    ].reset_index()  # filter out singleton protein groups\n",
+    "\n",
+    "    ad_relaxed_results_table[\"mol\"] = ad_relaxed_results_table[\"inchi_crystal\"].apply(\n",
+    "        lambda x: Chem.MolFromInchi(x)\n",
+    "    )\n",
+    "    ad_relaxed_results_table[\"fp\"] = ad_relaxed_results_table[\"mol\"].apply(\n",
+    "        lambda m: Chem.RDKFingerprint(m) if m is not None else None\n",
+    "    )\n",
+    "    ad_relaxed_results_table = pd.concat(\n",
+    "        [\n",
+    "            group\n",
+    "            for _, group in ad_relaxed_results_table.groupby(\"protein_id\")\n",
+    "            if group_has_low_similarity(group, threshold=0.6)\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    ad_unrelaxed_results_table = globals()[f\"results_table_{repeat_index}\"][\n",
+    "        (globals()[f\"results_table_{repeat_index}\"][\"dataset\"] == \"PoseBusters Benchmark set\")\n",
+    "        & (globals()[f\"results_table_{repeat_index}\"][\"post-processing\"] == \"none\")\n",
+    "        & (globals()[f\"results_table_{repeat_index}\"][\"protein_id\"] != \"Unknown\")\n",
+    "    ]\n",
+    "    ad_unrelaxed_results_table.loc[\n",
+    "        :, \"protein_group_size\"\n",
+    "    ] = ad_unrelaxed_results_table.protein_id.value_counts()[\n",
+    "        ad_unrelaxed_results_table.protein_id\n",
+    "    ].values\n",
+    "    ad_unrelaxed_results_table = ad_unrelaxed_results_table[\n",
+    "        ad_unrelaxed_results_table.protein_group_size > 1\n",
+    "    ].reset_index()  # filter out singleton protein groups\n",
+    "\n",
+    "    ad_unrelaxed_results_table[\"mol\"] = ad_unrelaxed_results_table[\"inchi_crystal\"].apply(\n",
+    "        lambda x: Chem.MolFromInchi(x)\n",
+    "    )\n",
+    "    ad_unrelaxed_results_table[\"fp\"] = ad_unrelaxed_results_table[\"mol\"].apply(\n",
+    "        lambda m: Chem.RDKFingerprint(m) if m is not None else None\n",
+    "    )\n",
+    "    ad_unrelaxed_results_table = pd.concat(\n",
+    "        [\n",
+    "            group\n",
+    "            for _, group in ad_unrelaxed_results_table.groupby(\"protein_id\")\n",
+    "            if group_has_low_similarity(group, threshold=0.6)\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    combined_data_list.append(pd.concat([ad_relaxed_results_table, ad_unrelaxed_results_table]))\n",
+    "\n",
+    "combined_relaxed_data = (\n",
+    "    pd.concat(combined_data_list).sort_values(\"method_assignment_index\").reset_index(drop=True)\n",
+    ")\n",
+    "\n",
+    "assert (\n",
+    "    len(combined_relaxed_data.method.unique()) == 1\n",
+    "), f\"Expected only one method in the combined data, but found: {combined_relaxed_data.method.unique()}\"\n",
+    "\n",
+    "# define font properties\n",
+    "plt.rcParams[\"font.size\"] = 12\n",
+    "plt.rcParams[\"axes.labelsize\"] = 14\n",
+    "\n",
+    "# set the size of the figure\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "\n",
+    "# create a violin plot grouped by protein_id\n",
+    "sns.violinplot(\n",
+    "    x=\"protein_id\",\n",
+    "    y=\"rmsd\",\n",
+    "    hue=\"post-processing\",\n",
+    "    # ignore outliers for better readability\n",
+    "    data=combined_relaxed_data[combined_relaxed_data[\"rmsd\"] < 10],\n",
+    "    split=True,\n",
+    "    inner=\"quartile\",\n",
+    "    palette=colors,\n",
+    "    cut=0,\n",
+    ")\n",
+    "\n",
+    "# set labels and title\n",
+    "plt.xlabel(\"UniProt ID\")\n",
+    "plt.ylabel(\"RMSD\")\n",
+    "\n",
+    "# rotate x-axis labels for better readability\n",
+    "plt.xticks(rotation=45, ha=\"right\")\n",
+    "\n",
+    "# display legend outside the plot\n",
+    "plt.legend(title=\"Post-processing\", bbox_to_anchor=(1.05, 1), loc=\"best\")\n",
+    "\n",
+    "# display the plots\n",
+    "plt.tight_layout()\n",
+    "plt.savefig(\n",
+    "    \"posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png\", dpi=300\n",
+    ")\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "FlowDock",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png b/notebooks/posebusters_benchmark_structure_prediction_relaxed_bar_chart.png
index 6c2c23981196f4a788fbfec76e4bbf5fed1339e0..2f894ebdd8a6359152169fa69e3988be491207f8 100644
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diff --git a/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png b/notebooks/posebusters_benchmark_structure_prediction_relaxed_rmsd_violin_plot_by_protein.png
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HcmV?d00001

diff --git a/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb b/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb
index 46ec209..0c4f9b9 100644
--- a/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb
+++ b/notebooks/posebusters_benchmark_structure_prediction_results_plotting.ipynb
@@ -69,6 +69,7 @@
     "    \"flowdock_hp\",\n",
     "    \"flowdock_aft\",\n",
     "    \"flowdock_esmfold\",\n",
+    "    \"flowdock_chai\",\n",
     "    \"flowdock\",\n",
     "]\n",
     "max_num_repeats_per_method = 3\n",
@@ -88,6 +89,7 @@
     "globals()[\"flowdock_esmfold_output_dir\"] = os.path.join(\n",
     "    \"..\", \"forks\", \"FlowDock\", \"esmfold_inference\"\n",
     ")\n",
+    "globals()[\"flowdock_chai_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"chai_inference\")\n",
     "globals()[\"flowdock_output_dir\"] = os.path.join(\"..\", \"forks\", \"FlowDock\", \"inference\")\n",
     "\n",
     "for repeat_index in range(1, max_num_repeats_per_method + 1):\n",
@@ -231,6 +233,22 @@
     "        )\n",
     "    )\n",
     "\n",
+    "    # FlowDock-Chai results\n",
+    "    globals()[f\"flowdock_chai_posebusters_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_chai_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "    globals()[f\"flowdock_chai_posebusters_relaxed_bust_results_csv_filepath_{repeat_index}\"] = (\n",
+    "        os.path.join(\n",
+    "            globals()[\"flowdock_chai_output_dir\"],\n",
+    "            f\"flowdock_posebusters_benchmark_outputs_{repeat_index}_relaxed\",\n",
+    "            \"bust_results.csv\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "    # FlowDock results\n",
     "    globals()[f\"flowdock_posebusters_bust_results_csv_filepath_{repeat_index}\"] = os.path.join(\n",
     "        globals()[\"flowdock_output_dir\"],\n",
@@ -257,6 +275,7 @@
     "    \"flowdock_hp\": \"FlowDock-HP\",\n",
     "    \"flowdock_aft\": \"FlowDock-AFT\",\n",
     "    \"flowdock_esmfold\": \"FlowDock-ESMFold\",\n",
+    "    \"flowdock_chai\": \"FlowDock-Chai-1\",\n",
     "    \"flowdock\": \"FlowDock-AF3\",\n",
     "}\n",
     "\n",
@@ -271,6 +290,7 @@
     "    \"flowdock_hp\": \"DL-based blind\",\n",
     "    \"flowdock_aft\": \"DL-based blind\",\n",
     "    \"flowdock_esmfold\": \"DL-based blind\",\n",
+    "    \"flowdock_chai\": \"DL-based blind\",\n",
     "    \"flowdock\": \"DL-based blind\",\n",
     "}\n",
     "\n",
@@ -544,7 +564,7 @@
     "colors = [\"#FB8072\", \"#BEBADA\"]\n",
     "\n",
     "bar_width = 0.5\n",
-    "r1 = [item - 0.25 for item in range(2, 24, 2)]\n",
+    "r1 = [item - 0.25 for item in range(2, 26, 2)]\n",
     "r2 = [x + bar_width for x in r1]\n",
     "\n",
     "(\n",
@@ -736,8 +756,8 @@
     "posebusters_relaxed_rmsd_lt_2_and_pb_valid_data_std.fillna(0, inplace=True)\n",
     "\n",
     "# define font properties\n",
-    "plt.rcParams[\"font.size\"] = 24\n",
-    "plt.rcParams[\"axes.labelsize\"] = 26\n",
+    "plt.rcParams[\"font.size\"] = 20\n",
+    "plt.rcParams[\"axes.labelsize\"] = 22\n",
     "\n",
     "# create the figure and a list of axes\n",
     "fig, axis = plt.subplots(figsize=(34, 14))\n",
@@ -788,7 +808,7 @@
     "\n",
     "# add labels, titles, ticks, etc.\n",
     "axis.set_ylabel(\"Percentage of predictions\")\n",
-    "axis.set_xlim(1, 23 + 0.1)\n",
+    "axis.set_xlim(1, 25 + 0.1)\n",
     "axis.set_ylim(0, 125)\n",
     "\n",
     "assert len(posebusters_rmsd_lt_2_bar) == len(posebusters_rmsd_lt_2_and_pb_valid_bar), (\n",
@@ -861,7 +881,7 @@
     "axis.grid(axis=\"y\", color=\"#EAEFF8\")\n",
     "axis.set_axisbelow(True)\n",
     "\n",
-    "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 13, 14, 16, 18, 20, 22])\n",
+    "axis.set_xticks([2, 2 + 1e-3, 4, 6, 8, 10, 12, 14, 14 + 1e-3, 16, 18, 20, 22, 24])\n",
     "axis.set_xticks([1 + 0.1], minor=True)\n",
     "axis.set_xticklabels(\n",
     "    [\n",
@@ -872,11 +892,12 @@
     "        \"RoseTTAFold-AA\",\n",
     "        \"AF3-Single-Seq\",\n",
     "        \"Chai-1-Single-Seq\",\n",
-    "        \"DL-based blind\",\n",
     "        \"NeuralPLexer\",\n",
+    "        \"DL-based blind\",\n",
     "        \"FlowDock-HP\",\n",
     "        \"FlowDock-AFT\",\n",
     "        \"FlowDock-ESMFold\",\n",
+    "        \"FlowDock-Chai-1\",\n",
     "        \"FlowDock-AF3\",\n",
     "    ]\n",
     ")\n",
@@ -889,7 +910,7 @@
     "axis.tick_params(axis=\"y\", which=\"major\", left=\"off\", right=\"on\", color=\"#EAEFF8\")\n",
     "\n",
     "# vertical alignment of xtick labels\n",
-    "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
+    "vert_alignments = [0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.1, 0.0, 0.0, 0.0, 0.0, 0.0]\n",
     "for tick, y in zip(axis.get_xticklabels(), vert_alignments):\n",
     "    tick.set_y(y)\n",
     "\n",
@@ -934,7 +955,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.10.16"
   }
  },
  "nbformat": 4,
diff --git a/scripts/esmfold_prior_plinder_finetuning.sh b/scripts/esmfold_prior_plinder_finetuning.sh
new file mode 100644
index 0000000..172344e
--- /dev/null
+++ b/scripts/esmfold_prior_plinder_finetuning.sh
@@ -0,0 +1,64 @@
+#!/bin/bash -l
+######################### Batch Headers #########################
+#SBATCH --partition chengji-lab-gpu    # NOTE: use reserved partition `chengji-lab-gpu` to use reserved A100 or H100 GPUs
+#SBATCH --account chengji-lab  # NOTE: this must be specified to use the reserved partition above
+#SBATCH --nodes=1              # NOTE: this needs to match Lightning's `Trainer(num_nodes=...)`
+#SBATCH --gres gpu:1           # request A100/H100 GPU resource(s)
+#SBATCH --ntasks-per-node=1    # NOTE: this needs to be `1` on SLURM clusters when using Lightning's `ddp_spawn` strategy`; otherwise, set to match Lightning's quantity of `Trainer(devices=...)`
+#SBATCH --mem=59G              # NOTE: use `--mem=0` to request all memory "available" on the assigned node
+#SBATCH -t 2-00:00:00          # time limit for the job (up to 2 days: `2-00:00:00`)
+#SBATCH -J esmfold_prior_plinder_finetuning    # job name
+#SBATCH --output=R-%x.%j.out   # output log file
+#SBATCH --error=R-%x.%j.err    # error log file
+
+module purge
+module load cuda/11.8.0_gcc_9.5.0
+
+# determine location of the project directory
+use_private_project_dir=false # NOTE: customize as needed
+if [ "$use_private_project_dir" = true ]; then
+    project_dir="/home/acmwhb/data/Repositories/Lab_Repositories/FlowDock"
+else
+    project_dir="/cluster/pixstor/chengji-lab/acmwhb/Repositories/Lab_Repositories/FlowDock"
+fi
+
+# shellcheck source=/dev/null
+source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh
+conda activate "$project_dir"/FlowDock/
+
+# Reference Conda system libraries
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+
+echo "Calling flowdock/train.py!"
+cd "$project_dir" || exit
+srun python3 flowdock/train.py \
+    callbacks.last_model_checkpoint.filename=null \
+    callbacks.last_model_checkpoint.every_n_train_steps=200 \
+    callbacks.last_model_checkpoint.every_n_epochs=null \
+    ckpt_path="$(realpath 'logs/train/runs/2025-03-17_17-39-39/checkpoints/169-562000.ckpt')" \
+    data=plinder \
+    experiment='flowdock_fm' \
+    environment=slurm \
+    logger=wandb \
+    logger.wandb.entity='bml-lab' \
+    logger.wandb.group='FlowDock-FM' \
+    +logger.wandb.name='2025-03-17_17:00:00-ESMFold-Prior-PLINDER-Finetuning' \
+    +logger.wandb.id='1x2k5a79' \
+    model.cfg.prior_type=esmfold \
+    model.cfg.task.freeze_score_head=false \
+    model.cfg.task.freeze_affinity=true \
+    paths.output_dir="$(realpath 'logs/train/runs/2025-03-17_17-39-39')" \
+    strategy=ddp \
+    trainer=ddp \
+    +trainer.accumulate_grad_batches=4 \
+    trainer.devices=1 \
+    trainer.num_nodes=1
+echo "Finished calling flowdock/train.py!"
+
+# NOTE: the following commands must be used to resume training from a checkpoint
+# ckpt_path="$(realpath 'logs/train/runs/2025-03-17_17-39-39/checkpoints/169-562000.ckpt')" \
+# paths.output_dir="$(realpath 'logs/train/runs/2025-03-17_17-39-39')" \
+
+# NOTE: the following commands may be used to speed up training
+# model.compile=false \
+# +trainer.precision=bf16-mixed
diff --git a/scripts/esmfold_prior_tiered_training.sh b/scripts/esmfold_prior_tiered_training.sh
index bc71a6c..2593faf 100644
--- a/scripts/esmfold_prior_tiered_training.sh
+++ b/scripts/esmfold_prior_tiered_training.sh
@@ -30,6 +30,9 @@ fi
 source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh
 conda activate "$project_dir"/FlowDock/
 
+# Reference Conda system libraries
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+
 # NOTE: for tiered training, start by setting `model.cfg.task.freeze_score_head=false` and `model.cfg.task.freeze_affinity=true`,
 # and once the model's score head has been trained to convergence, resume training with `model.cfg.task.freeze_score_head=true` and `model.cfg.task.freeze_affinity=false`
 
diff --git a/scripts/esmfold_prior_training.sh b/scripts/esmfold_prior_training.sh
index b6d88ac..6719383 100644
--- a/scripts/esmfold_prior_training.sh
+++ b/scripts/esmfold_prior_training.sh
@@ -30,6 +30,9 @@ fi
 source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh
 conda activate "$project_dir"/FlowDock/
 
+# Reference Conda system libraries
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+
 echo "Calling flowdock/train.py!"
 cd "$project_dir" || exit
 srun python3 flowdock/train.py \
diff --git a/scripts/harmonic_prior_training.sh b/scripts/harmonic_prior_training.sh
index b6cc386..f7e05c2 100644
--- a/scripts/harmonic_prior_training.sh
+++ b/scripts/harmonic_prior_training.sh
@@ -30,6 +30,9 @@ fi
 source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh
 conda activate "$project_dir"/FlowDock/
 
+# Reference Conda system libraries
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+
 echo "Calling flowdock/train.py!"
 cd "$project_dir" || exit
 srun python3 flowdock/train.py \
diff --git a/scripts/plinder_download.sh b/scripts/plinder_download.sh
new file mode 100644
index 0000000..b117b73
--- /dev/null
+++ b/scripts/plinder_download.sh
@@ -0,0 +1,38 @@
+#!/bin/bash -l
+######################### Batch Headers #########################
+#SBATCH --partition general    # NOTE: use reserved partition `chengji-lab-gpu` to use reserved A100 or H100 GPUs
+#SBATCH --account chengji-lab  # NOTE: this must be specified to use the reserved partition above
+#SBATCH --nodes=1              # NOTE: this needs to match Lightning's `Trainer(num_nodes=...)`
+#SBATCH --ntasks-per-node=1    # NOTE: this needs to be `1` on SLURM clusters when using Lightning's `ddp_spawn` strategy`; otherwise, set to match Lightning's quantity of `Trainer(devices=...)`
+#SBATCH --mem=59G              # NOTE: use `--mem=0` to request all memory "available" on the assigned node
+#SBATCH -t 0-02:00:00          # time limit for the job (up to 2 days: `2-00:00:00`)
+#SBATCH -J plinder_download    # job name
+#SBATCH --output=R-%x.%j.out   # output log file
+#SBATCH --error=R-%x.%j.err    # error log file
+
+module purge
+module load cuda/11.8.0_gcc_9.5.0
+
+# determine location of the project directory
+use_private_project_dir=false # NOTE: customize as needed
+if [ "$use_private_project_dir" = true ]; then
+    project_dir="/home/acmwhb/data/Repositories/Lab_Repositories/FlowDock"
+else
+    project_dir="/cluster/pixstor/chengji-lab/acmwhb/Repositories/Lab_Repositories/FlowDock"
+fi
+
+# shellcheck source=/dev/null
+source /cluster/pixstor/chengji-lab/acmwhb/miniforge3/etc/profile.d/conda.sh
+conda activate "$project_dir"/FlowDock/
+
+# Reference Conda system libraries
+export LD_LIBRARY_PATH="$CONDA_PREFIX/lib:$LD_LIBRARY_PATH"
+
+# determine location of PLINDER dataset
+export PLINDER_MOUNT="$project_dir/data/PLINDER" # NOTE: customize as needed
+mkdir -p "$PLINDER_MOUNT" # create the directory if it doesn't exist
+
+echo "Downloading PLINDER to $PLINDER_MOUNT!"
+cd "$project_dir" || exit
+plinder_download -y
+echo "Finished downloading PLINDER to $PLINDER_MOUNT!"

From 7a81e7f24a5996d8031e7c30c27b3707ded91a2e Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Fri, 21 Mar 2025 15:41:08 -0500
Subject: [PATCH 5/6] Update Zenodo record

---
 README.md | 44 ++++++++++++++++++++++++++------------------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/README.md b/README.md
index 5f6d70f..0a845bb 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
 
 <!-- [![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/paper/2020) -->
 
-[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14660031.svg)](https://doi.org/10.5281/zenodo.14660031)
+[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15066390.svg)](https://doi.org/10.5281/zenodo.15066390)
 
 <img src="./img/FlowDock.png" width="600">
 
@@ -92,7 +92,7 @@ cd ../
 
 ```bash
 # pretrained FlowDock weights
-wget https://zenodo.org/records/14660031/files/flowdock_checkpoints.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_checkpoints.tar.gz
 tar -xzf flowdock_checkpoints.tar.gz
 rm flowdock_checkpoints.tar.gz
 ```
@@ -106,19 +106,19 @@ tar -xzf flowdock_data_cache.tar.gz
 rm flowdock_data_cache.tar.gz
 
 # cached data for PDBBind, Binding MOAD, DockGen, and the PDB-based van der Mers (vdM) dataset
-wget https://zenodo.org/records/14660031/files/flowdock_pdbbind_data.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_pdbbind_data.tar.gz
 tar -xzf flowdock_pdbbind_data.tar.gz
 rm flowdock_pdbbind_data.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_moad_data.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_moad_data.tar.gz
 tar -xzf flowdock_moad_data.tar.gz
 rm flowdock_moad_data.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_dockgen_data.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_dockgen_data.tar.gz
 tar -xzf flowdock_dockgen_data.tar.gz
 rm flowdock_dockgen_data.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_pdbsidechain_data.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_pdbsidechain_data.tar.gz
 tar -xzf flowdock_pdbsidechain_data.tar.gz
 rm flowdock_pdbsidechain_data.tar.gz
 ```
@@ -130,7 +130,7 @@ rm flowdock_pdbsidechain_data.tar.gz
 <details>
 
 **NOTE:** The following steps (besides downloading PDBBind and Binding MOAD's PDB files) are only necessary if one wants to fully process each of the following datasets manually.
-Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/14660031).
+Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/15066390).
 
 Download data
 
@@ -302,47 +302,55 @@ Download baseline method predictions and results
 
 ```bash
 # cached predictions and evaluation metrics for reproducing structure prediction paper results
-wget https://zenodo.org/records/14660031/files/alphafold3_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/alphafold3_baseline_method_predictions.tar.gz
 tar -xzf alphafold3_baseline_method_predictions.tar.gz
 rm alphafold3_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/chai_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/chai_baseline_method_predictions.tar.gz
 tar -xzf chai_baseline_method_predictions.tar.gz
 rm chai_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/diffdock_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/diffdock_baseline_method_predictions.tar.gz
 tar -xzf diffdock_baseline_method_predictions.tar.gz
 rm diffdock_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/dynamicbind_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/dynamicbind_baseline_method_predictions.tar.gz
 tar -xzf dynamicbind_baseline_method_predictions.tar.gz
 rm dynamicbind_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_baseline_method_predictions.tar.gz
 tar -xzf flowdock_baseline_method_predictions.tar.gz
 rm flowdock_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_aft_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_aft_baseline_method_predictions.tar.gz
 tar -xzf flowdock_aft_baseline_method_predictions.tar.gz
 rm flowdock_aft_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_esmfold_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_pft_baseline_method_predictions.tar.gz
+tar -xzf flowdock_pft_baseline_method_predictions.tar.gz
+rm flowdock_pft_baseline_method_predictions.tar.gz
+
+wget https://zenodo.org/records/15066390/files/flowdock_esmfold_baseline_method_predictions.tar.gz
 tar -xzf flowdock_esmfold_baseline_method_predictions.tar.gz
 rm flowdock_esmfold_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/flowdock_hp_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/flowdock_chai_baseline_method_predictions.tar.gz
+tar -xzf flowdock_chai_baseline_method_predictions.tar.gz
+rm flowdock_chai_baseline_method_predictions.tar.gz
+
+wget https://zenodo.org/records/15066390/files/flowdock_hp_baseline_method_predictions.tar.gz
 tar -xzf flowdock_hp_baseline_method_predictions.tar.gz
 rm flowdock_hp_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/neuralplexer_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/neuralplexer_baseline_method_predictions.tar.gz
 tar -xzf neuralplexer_baseline_method_predictions.tar.gz
 rm neuralplexer_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/vina_p2rank_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/vina_p2rank_baseline_method_predictions.tar.gz
 tar -xzf vina_p2rank_baseline_method_predictions.tar.gz
 rm vina_p2rank_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/14660031/files/rfaa_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066390/files/rfaa_baseline_method_predictions.tar.gz
 tar -xzf rfaa_baseline_method_predictions.tar.gz
 rm rfaa_baseline_method_predictions.tar.gz
 ```

From 021e9b7c0f323bcaa8fd472816ec691c22b0a1c3 Mon Sep 17 00:00:00 2001
From: Alex Morehead <acmwhb@missouri.edu>
Date: Fri, 21 Mar 2025 15:57:48 -0500
Subject: [PATCH 6/6] Update checkpoints

---
 README.md | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/README.md b/README.md
index 0a845bb..3bd45fd 100644
--- a/README.md
+++ b/README.md
@@ -12,7 +12,7 @@
 
 <!-- [![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/paper/2020) -->
 
-[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15066390.svg)](https://doi.org/10.5281/zenodo.15066390)
+[![Data DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.15066450.svg)](https://doi.org/10.5281/zenodo.15066450)
 
 <img src="./img/FlowDock.png" width="600">
 
@@ -92,7 +92,7 @@ cd ../
 
 ```bash
 # pretrained FlowDock weights
-wget https://zenodo.org/records/15066390/files/flowdock_checkpoints.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_checkpoints.tar.gz
 tar -xzf flowdock_checkpoints.tar.gz
 rm flowdock_checkpoints.tar.gz
 ```
@@ -106,19 +106,19 @@ tar -xzf flowdock_data_cache.tar.gz
 rm flowdock_data_cache.tar.gz
 
 # cached data for PDBBind, Binding MOAD, DockGen, and the PDB-based van der Mers (vdM) dataset
-wget https://zenodo.org/records/15066390/files/flowdock_pdbbind_data.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_pdbbind_data.tar.gz
 tar -xzf flowdock_pdbbind_data.tar.gz
 rm flowdock_pdbbind_data.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_moad_data.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_moad_data.tar.gz
 tar -xzf flowdock_moad_data.tar.gz
 rm flowdock_moad_data.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_dockgen_data.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_dockgen_data.tar.gz
 tar -xzf flowdock_dockgen_data.tar.gz
 rm flowdock_dockgen_data.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_pdbsidechain_data.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_pdbsidechain_data.tar.gz
 tar -xzf flowdock_pdbsidechain_data.tar.gz
 rm flowdock_pdbsidechain_data.tar.gz
 ```
@@ -130,7 +130,7 @@ rm flowdock_pdbsidechain_data.tar.gz
 <details>
 
 **NOTE:** The following steps (besides downloading PDBBind and Binding MOAD's PDB files) are only necessary if one wants to fully process each of the following datasets manually.
-Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/15066390).
+Otherwise, preprocessed versions of each dataset can be found on [Zenodo](https://zenodo.org/records/15066450).
 
 Download data
 
@@ -302,55 +302,55 @@ Download baseline method predictions and results
 
 ```bash
 # cached predictions and evaluation metrics for reproducing structure prediction paper results
-wget https://zenodo.org/records/15066390/files/alphafold3_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/alphafold3_baseline_method_predictions.tar.gz
 tar -xzf alphafold3_baseline_method_predictions.tar.gz
 rm alphafold3_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/chai_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/chai_baseline_method_predictions.tar.gz
 tar -xzf chai_baseline_method_predictions.tar.gz
 rm chai_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/diffdock_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/diffdock_baseline_method_predictions.tar.gz
 tar -xzf diffdock_baseline_method_predictions.tar.gz
 rm diffdock_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/dynamicbind_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/dynamicbind_baseline_method_predictions.tar.gz
 tar -xzf dynamicbind_baseline_method_predictions.tar.gz
 rm dynamicbind_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_baseline_method_predictions.tar.gz
 tar -xzf flowdock_baseline_method_predictions.tar.gz
 rm flowdock_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_aft_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_aft_baseline_method_predictions.tar.gz
 tar -xzf flowdock_aft_baseline_method_predictions.tar.gz
 rm flowdock_aft_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_pft_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_pft_baseline_method_predictions.tar.gz
 tar -xzf flowdock_pft_baseline_method_predictions.tar.gz
 rm flowdock_pft_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_esmfold_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_esmfold_baseline_method_predictions.tar.gz
 tar -xzf flowdock_esmfold_baseline_method_predictions.tar.gz
 rm flowdock_esmfold_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_chai_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_chai_baseline_method_predictions.tar.gz
 tar -xzf flowdock_chai_baseline_method_predictions.tar.gz
 rm flowdock_chai_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/flowdock_hp_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/flowdock_hp_baseline_method_predictions.tar.gz
 tar -xzf flowdock_hp_baseline_method_predictions.tar.gz
 rm flowdock_hp_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/neuralplexer_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/neuralplexer_baseline_method_predictions.tar.gz
 tar -xzf neuralplexer_baseline_method_predictions.tar.gz
 rm neuralplexer_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/vina_p2rank_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/vina_p2rank_baseline_method_predictions.tar.gz
 tar -xzf vina_p2rank_baseline_method_predictions.tar.gz
 rm vina_p2rank_baseline_method_predictions.tar.gz
 
-wget https://zenodo.org/records/15066390/files/rfaa_baseline_method_predictions.tar.gz
+wget https://zenodo.org/records/15066450/files/rfaa_baseline_method_predictions.tar.gz
 tar -xzf rfaa_baseline_method_predictions.tar.gz
 rm rfaa_baseline_method_predictions.tar.gz
 ```