07_process_sampled_compounds.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You need to process all 200 million sampled SMILES. The provided script '07a_process_sampled_compounds.py' does the job for you. It takes the epoch number as an argument and then process the SMILES and calculates the fingerprints, and basic descriptors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is very easy to parallize and you can use your cluster and submit every job to it. E.g: Copy psate the commands to the cluster and run it"
   ]
  },
  {
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   "execution_count": 4,
   "metadata": {
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    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "srun python 07a_process_sampled_compounds.py 1 &\n",
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      "srun python 07a_process_sampled_compounds.py 200 &\n"
     ]
    }
   ],
   "source": [
    "for i in range(1,201):\n",
    "    command = f\"srun python 07a_process_sampled_compounds.py {i} &\"\n",
    "    print(command)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next step is to run the nearest neighbor analysis. For this you can either run the '08_nn_calculation.ipynb' locally or use the '08a_nn_calculation.py' script analogous to the '08a_process_sampled_compounds.py' script"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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