apahl/ecfc_stereo.ipynb

## ecfc_stereo.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "champion-shipping",
   "metadata": {},
   "source": [
    "# Are RDKit's ECFC4 fingerprint stereospecific?\n",
    "\n",
    "Following an internal discussion, the above question should be answered."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "negative-assessment",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-12T13:18:17.054550Z",
     "iopub.status.busy": "2021-02-12T13:18:17.054271Z",
     "iopub.status.idle": "2021-02-12T13:18:17.237104Z",
     "shell.execute_reply": "2021-02-12T13:18:17.236704Z",
     "shell.execute_reply.started": "2021-02-12T13:18:17.054477Z"
    }
   },
   "outputs": [],
   "source": [
    "from rdkit.Chem import AllChem as Chem\n",
    "from rdkit import DataStructs\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "running-delivery",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-12T13:18:17.237766Z",
     "iopub.status.busy": "2021-02-12T13:18:17.237640Z",
     "iopub.status.idle": "2021-02-12T13:18:17.255595Z",
     "shell.execute_reply": "2021-02-12T13:18:17.255152Z",
     "shell.execute_reply.started": "2021-02-12T13:18:17.237752Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles = [\"O=C1NCCC(O)C1O\", \"O=C1NCC[C@@H](O)[C@H]1O\", \"O=C1NCC[C@@H](O)[C@@H]1O\", \"O=C1NCC[C@H](O)[C@@H]1O\"]\n",
    "mols = [Chem.MolFromSmiles(x) for x in smiles]\n",
    "Draw.MolsToGridImage(mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "handy-supervision",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-12T13:18:17.256364Z",
     "iopub.status.busy": "2021-02-12T13:18:17.256225Z",
     "iopub.status.idle": "2021-02-12T13:18:17.259538Z",
     "shell.execute_reply": "2021-02-12T13:18:17.259091Z",
     "shell.execute_reply.started": "2021-02-12T13:18:17.256348Z"
    }
   },
   "outputs": [],
   "source": [
    "fps = [Chem.GetMorganFingerprint(x, 2) for x in mols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "considered-compression",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-02-12T13:18:17.260260Z",
     "iopub.status.busy": "2021-02-12T13:18:17.260134Z",
     "iopub.status.idle": "2021-02-12T13:18:17.263738Z",
     "shell.execute_reply": "2021-02-12T13:18:17.263314Z",
     "shell.execute_reply.started": "2021-02-12T13:18:17.260245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "for ix1, fp1 in enumerate(fps):\n",
    "    for fp2 in fps[ix1+1:]:\n",
    "        print(DataStructs.TanimotoSimilarity(fp1, fp2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "circular-algebra",
   "metadata": {},
   "source": [
    "Answer: No."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "super-denver",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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