{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thresholds for \"similar\" in fingerprints the RDKit supports\n",
    "\n",
    "\n",
    "<table>\n",
    "\n",
    "</table>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021.03.1\n",
      "Fri May 21 08:09:31 2021\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/glandrum/miniconda3/envs/rdkit_blog/lib/python3.9/site-packages/IPython/core/magics/pylab.py:159: UserWarning: pylab import has clobbered these variables: ['random']\n",
      "`%matplotlib` prevents importing * from pylab and numpy\n",
      "  warn(\"pylab import has clobbered these variables: %s\"  % clobbered +\n"
     ]
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import rdMolDescriptors\n",
    "from rdkit.Avalon import pyAvalonTools\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit import rdBase\n",
    "from rdkit import DataStructs\n",
    "from collections import defaultdict\n",
    "import pickle,random,gzip\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "print(rdBase.rdkitVersion)\n",
    "import time\n",
    "print(time.asctime())\n",
    "%pylab inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read in the data\n",
    "\n",
    "Sets of compounds from ChEMBL documents with a fuzzy MCS scaffold. From this blog post:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will use a namedtuple to return the results\n",
    "from collections import namedtuple\n",
    "MCSRes=namedtuple('MCSRes',('smarts','numAtoms','numMols','avgNumMolAtoms','mcsTime'))\n",
    "\n",
    "data = pickle.load(open('../data/scaffolds_revisited_again.pkl','rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "402\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "((1528353, 'CHEMBL3706284', 98, \"Radioligand Binding Assay: HEK293 stably expressing human orexin 2 receptor (Genebank accession numberNM_001526) were grown to confluency in DMEM (Hy ... (715 characters truncated) ... petition binding experiments in 96 well polypropylene plates were performed using [3H]-EMPA (Moraveck Corporation, specific activity = 29.6 Ci/mmol).\"),\n",
       " [('CHEMBL3704924', 'Cc1cc(C)nc(N[C@@H]2CCCN(C(=O)c3ccc(F)cc3-n3nccn3)[C@H]2C)n1'),\n",
       "  ('CHEMBL3704925', 'Cc1cc(C)nc(N[C@@H]2CCCN(C(=O)c3c(F)cccc3-c3ncccn3)[C@H]2C)n1'),\n",
       "  ('CHEMBL3704926', 'Cc1cc(C)nc(N[C@@H]2CCCN(C(=O)c3c(F)cccc3-n3nccn3)[C@H]2C)n1'),\n",
       "  ('CHEMBL3704927', 'Cc1cc(C)nc(N[C@@H]2CCCN(C(=O)c3ccccc3-n3nccn3)[C@H]2C)n1'),\n",
       "  ('CHEMBL3704928', 'Cc1cc(C)nc(N[C@@H]2CCCN(C(=O)c3cc(F)ccc3-n3nccn3)[C@H]2C)n1'),\n",
       "  ('CHEMBL3704929', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3nc(C)cc(C)n3)[C@@H]2C)c1'),\n",
       "  ('CHEMBL3704930', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3cc(C)nc(C(F)(F)F)n3)[C@@H]2C)c1'),\n",
       "  ('CHEMBL3704931', 'Cc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Nc3cc(C)nc(C(F)(F)F)n3)[C@@H]2C)c1'),\n",
       "  ('CHEMBL3704932', 'Cc1cc(N[C@@H]2CCCN(C(=O)c3cc(F)ccc3-c3ncccn3)[C@H]2C)nc(C(F)(F)F)n1'),\n",
       "  ('CHEMBL3704933', 'C[C@H]1[C@H](Nc2nccc(C(F)(F)F)n2)CCCN1C(=O)c1c(F)cccc1-n1nccn1'),\n",
       "  ('CHEMBL3704934', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3nccc(C(F)(F)F)n3)[C@@H]2C)c(-n2ccnn2)n1'),\n",
       "  ('CHEMBL3704935', 'C[C@H]1[C@H](Nc2ccc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704936', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3704936', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3704937', 'Cc1ccc(C(=O)N2CCC[C@H](Nc3ccc(C(F)(F)F)cn3)[C@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3704938', 'CCOc1ccc(C)nc1C(=O)N1CCC[C@@H](Nc2ccc(C(F)(F)F)cn2)[C@@H]1C'),\n",
       "  ('CHEMBL3597969', 'C[C@H]1[C@H](Nc2ccc(C(F)(F)F)cn2)CCCN1C(=O)c1cc(Cl)ccc1-n1nccn1'),\n",
       "  ('CHEMBL3704939', 'Cc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3704940', 'COc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c1'),\n",
       "  ('CHEMBL3704941', 'Cc1cccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c1-n1nccn1'),\n",
       "  ('CHEMBL3704942', 'C[C@H]1[C@H](Nc2ccc(C(F)(F)F)cn2)CCCN1C(=O)c1ncccc1-n1nccn1'),\n",
       "  ('CHEMBL3704943', 'Cc1ccc(-c2ncco2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3704944', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3704945', 'C[C@H]1[C@H](Nc2ccc(C(F)(F)F)cn2)CCCN1C(=O)c1cc(F)ccc1-n1nccn1'),\n",
       "  ('CHEMBL3704943', 'Cc1ccc(-c2ncco2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3704946', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3704947', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-c2ncccn2)c1'),\n",
       "  ('CHEMBL3704948', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2ccc(C(F)(F)F)cn2)[C@@H]1C'),\n",
       "  ('CHEMBL3704949', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2ccc(C(F)(F)F)cn2)[C@@H]1C'),\n",
       "  ('CHEMBL3704950', 'C[C@H]1[C@H](Nc2ncc(Cl)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3640038', 'Cc1cnc(N[C@@H]2CCCN(C(=O)c3ccccc3-n3nccn3)[C@H]2C)nc1'),\n",
       "  ('CHEMBL3704951', 'C[C@H]1[C@H](Nc2ccc(Cl)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704952', 'C[C@H]1[C@H](Nc2ccc(C(F)(F)F)nn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704953', 'C[C@H]1[C@H](Nc2ccc(F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704954', 'C[C@H]1[C@H](Nc2ncc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704955', 'C[C@H]1[C@H](Nc2ccccn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704956', 'Cc1ccc(N[C@@H]2CCCN(C(=O)c3ccccc3-n3nccn3)[C@H]2C)nc1'),\n",
       "  ('CHEMBL3704957', 'C[C@H]1[C@H](Nc2cnc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704958', 'C[C@H]1[C@H](Nc2ncc3ccccc3n2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704959', 'C[C@H]1[C@H](Nc2ncc(F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704960', 'C[C@H]1[C@H](Nc2nc3ccccc3o2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704961', 'C[C@H]1[C@H](Nc2ccc(Br)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704962', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2ccc(Br)cn2)[C@@H]1C'),\n",
       "  ('CHEMBL3704963', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2ccc(Br)cn2)[C@@H]1C'),\n",
       "  ('CHEMBL3704964', 'C[C@H]1[C@H](Nc2nc3cc(Cl)ccc3o2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704965', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2nc3cc(Cl)ccc3o2)[C@@H]1C'),\n",
       "  ('CHEMBL3704966', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2nc3cc(Cl)ccc3o2)[C@@H]1C'),\n",
       "  ('CHEMBL3704967', 'C[C@H]1[C@H](Nc2cnc3ccccc3n2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3704968', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2cnc3ccccc3n2)[C@@H]1C'),\n",
       "  ('CHEMBL3704969', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2cnc3ccccc3n2)[C@@H]1C'),\n",
       "  ('CHEMBL3704970', 'C[C@H]1[C@H](Nc2nccc(-c3ccccc3)n2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669006', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2nccc(-c3ccccc3)n2)[C@@H]1C'),\n",
       "  ('CHEMBL3669007', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2nccc(-c3ccccc3)n2)[C@@H]1C'),\n",
       "  ('CHEMBL3669008', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2ccc(C(F)(F)F)cn2)[C@H]1C'),\n",
       "  ('CHEMBL3669009', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2ccc(C(F)(F)F)cn2)[C@H]1C'),\n",
       "  ('CHEMBL3669010', 'C[C@@H]1[C@H](Nc2ccc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669011', 'C[C@@H]1[C@H](Nc2ccc(Br)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669012', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2ccc(Br)cn2)[C@H]1C'),\n",
       "  ('CHEMBL3669013', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2ccc(Br)cn2)[C@H]1C'),\n",
       "  ('CHEMBL3639678', 'C[C@@H]1[C@H](Nc2nc3cc(Cl)ccc3o2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669014', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2nc3cc(Cl)ccc3o2)[C@H]1C'),\n",
       "  ('CHEMBL3669015', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2nc3cc(Cl)ccc3o2)[C@H]1C'),\n",
       "  ('CHEMBL3669016', 'C[C@@H]1[C@H](Nc2cnc3ccccc3n2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669017', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2cnc3ccccc3n2)[C@H]1C'),\n",
       "  ('CHEMBL3669018', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2cnc3ccccc3n2)[C@H]1C'),\n",
       "  ('CHEMBL3669019', 'C[C@@H]1[C@H](Nc2nccc(-c3ccccc3)n2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669020', 'COc1c(F)cccc1C(=O)N1CCC[C@@H](Nc2nccc(-c3ccccc3)n2)[C@H]1C'),\n",
       "  ('CHEMBL3669021', 'CCOc1ccccc1C(=O)N1CCC[C@@H](Nc2nccc(-c3ccccc3)n2)[C@H]1C'),\n",
       "  ('CHEMBL3905361', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669022', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)n1'),\n",
       "  ('CHEMBL3669023', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3905361', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669025', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3nc4ccccc4o3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669022', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)n1'),\n",
       "  ('CHEMBL3669026', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3nc4ccccc4o3)[C@@H]2C)c(-n2nccn2)n1'),\n",
       "  ('CHEMBL3669027', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3nc4ccccc4o3)[C@@H]2C)c(-n2ccnn2)n1'),\n",
       "  ('CHEMBL3669028', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ccc(Cl)cn3)[C@@H]2C)c(-c2ncco2)c1'),\n",
       "  ('CHEMBL3669029', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3669030', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669031', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ccc(Br)cn3)[C@@H]2C)c(-c2ncco2)c1'),\n",
       "  ('CHEMBL3669032', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3nc4ccccc4o3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3669033', 'COc1ccc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669034', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-c2ncco2)c1'),\n",
       "  ('CHEMBL3669035', 'Cc1ccc(-c2ncccn2)c(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3704957', 'C[C@H]1[C@H](Nc2cnc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669036', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3cnc(Cl)cn3)[C@@H]2C)c(-c2ncco2)c1'),\n",
       "  ('CHEMBL3704939', 'Cc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3669037', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3nc4ccccc4o3)[C@@H]2C)c(-c2ncco2)c1'),\n",
       "  ('CHEMBL3669038', 'COc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c1'),\n",
       "  ('CHEMBL3669039', 'C[C@H]1[C@H](Nc2cnc(C(F)(F)F)cn2)CCCN1C(=O)c1cc(F)ccc1-n1nccn1'),\n",
       "  ('CHEMBL3669040', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ncc(Cl)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669041', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ccc(Br)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669042', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2ccnn2)n1'),\n",
       "  ('CHEMBL3669024', 'Cc1cnc(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669043', 'COc1ccc(C(=O)N2CCC[C@@H](Nc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669044', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3cnc(C(F)(F)F)cn3)[C@@H]2C)c(-n2ccnn2)n1'),\n",
       "  ('CHEMBL3669045', 'COc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669046', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)n1'),\n",
       "  ('CHEMBL3669047', 'Cc1ccc(C(=O)N2CCC[C@@H](Nc3ccc(Cl)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3704951', 'C[C@H]1[C@H](Nc2ccc(Cl)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669048', 'Cc1cnc(C(=O)N2CCC[C@@H](Oc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669049', 'Cc1ccc(C(=O)N2CCC[C@@H](Oc3ncc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1'),\n",
       "  ('CHEMBL3669050', 'C[C@H]1[C@H](Oc2ncc(C(F)(F)F)cn2)CCCN1C(=O)c1ccccc1-n1nccn1'),\n",
       "  ('CHEMBL3669051', 'Cc1ccc(-n2nccn2)c(C(=O)N2CCC[C@@H](Oc3ncc(C(F)(F)F)cn3)[C@@H]2C)n1'),\n",
       "  ('CHEMBL3597970', 'Cc1ccc(C(=O)N2CCC[C@@H](Oc3ccc(C(F)(F)F)cn3)[C@@H]2C)c(-n2nccn2)c1')],\n",
       " MCSRes(smarts='[#8,#6,#7,#9]-&!@[#6]1:&@[#6,#7]:&@[#6]:&@[#6]:&@[#6,#7]:&@[#6]:&@1-&!@[#6](=&!@[#8])-&!@[#7]1-&@[#6]-&@[#6]-&@[#6]-&@[#6](-&@[#6]-&@1-&!@[#6])-&!@[#7,#8]-&!@[#6]', numAtoms=18, numMols=105, avgNumMolAtoms=array([30, 31, 30, 29, 30, 31, 34, 33, 34, 32, 32, 31, 32, 32, 32, 30, 32,\n",
       "        32, 33, 32, 31, 32, 33, 32, 32, 32, 33, 29, 29, 28, 28, 28, 31, 28,\n",
       "        31, 27, 28, 31, 31, 28, 30, 28, 26, 26, 31, 29, 29, 31, 29, 29, 33,\n",
       "        31, 31, 29, 29, 31, 28, 26, 26, 31, 29, 29, 31, 29, 29, 33, 31, 31,\n",
       "        32, 32, 32, 32, 31, 32, 31, 31, 29, 33, 32, 29, 32, 33, 32, 33, 31,\n",
       "        29, 32, 31, 33, 32, 29, 29, 32, 32, 33, 32, 33, 32, 29, 28, 32, 32,\n",
       "        31, 32, 32]), mcsTime=30.152477025985718),\n",
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      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(len(data))\n",
    "data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many of those have a scaffold which is at least 50% of the average number of atoms?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "301"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keep = [x for x in data if x[2].numAtoms>=np.mean(x[2].avgNumMolAtoms)/2]\n",
    "len(keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many compounds in total?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-104317c841c0>:1: DeprecationWarning: Calling np.sum(generator) is deprecated, and in the future will give a different result. Use np.sum(np.fromiter(generator)) or the python sum builtin instead.\n",
      "  sum(len(x[1]) for x in keep)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "20342"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(len(x[1]) for x in keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import ipyparallel as ipp\n",
    "    rc = ipp.Client()\n",
    "    dview = rc[:]\n",
    "    dview.execute('from rdkit import Chem')\n",
    "    dview.execute('from rdkit import Descriptors')\n",
    "    dview.execute('from rdkit import DataStructs')\n",
    "    dview.execute('from rdkit.Chem import rdMolDescriptors')\n",
    "    dview.execute('from rdkit.Avalon import pyAvalonTools')\n",
    "except:\n",
    "    print(\"could not use ipyparallel\")\n",
    "    dview = None\n",
    "\n",
    "def smi_to_fp(row,fpfn):\n",
    "    mol = Chem.MolFromSmiles(row[1])\n",
    "    return fpfn(mol)\n",
    "\n",
    "def calc_sims(i,fps):\n",
    "    return DataStructs.BulkTanimotoSimilarity(fps[i],fps[i+1:]) \n",
    "from collections import defaultdict\n",
    "def compareFPs(keep,fpfn,fpName,accum):\n",
    "    for assay,smis,mcs,_ in keep:\n",
    "        print(assay[1])\n",
    "        if dview is not None:\n",
    "            fps = dview.map_sync(lambda x,y=smi_to_fp,z=fpfn:y(x,z),smis)\n",
    "        else:\n",
    "            fps = [fpfn(Chem.MolFromSmiles(x)) for x in smis]\n",
    "        if dview is not None:\n",
    "            accums = dview.map_sync(lambda x,y=calc_sims,z=fps:y(x,z),range(len(fps)))\n",
    "        else:\n",
    "            accums = [calc_sims(i,fps) for i in range(len(fps))]\n",
    "        sims=[]\n",
    "        for v in accums:\n",
    "            sims.extend(v)\n",
    "        sl = sorted(sims)\n",
    "        np = len(sl)\n",
    "        res = {fpName:{}}\n",
    "        for bin in (0.05,.1,.2,.3,.4,.5,.6,.7,.8,.9,.95):\n",
    "            simv = sl[int(bin*np)]\n",
    "            print( bin,simv)\n",
    "            res[fpName][bin] = simv\n",
    "        accum[assay[1]].update(res)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "accum = defaultdict(dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MACCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.1 0.6438356164383562\n",
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      "0.3 0.7142857142857143\n",
      "0.4 0.7571428571428571\n",
      "0.5 0.7887323943661971\n",
      "0.6 0.821917808219178\n",
      "0.7 0.8571428571428571\n",
      "0.8 0.8983050847457628\n",
      "0.9 0.9354838709677419\n",
      "0.95 0.9672131147540983\n",
      "CHEMBL3705289\n",
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      "0.1 0.6140350877192983\n",
      "0.2 0.6470588235294118\n",
      "0.3 0.673469387755102\n",
      "0.4 0.7037037037037037\n",
      "0.5 0.7307692307692307\n",
      "0.6 0.7647058823529411\n",
      "0.7 0.8\n",
      "0.8 0.8367346938775511\n",
      "0.9 0.8979591836734694\n",
      "0.95 0.94\n",
      "CHEMBL3734283\n",
      "0.05 0.5818181818181818\n",
      "0.1 0.609375\n",
      "0.2 0.6607142857142857\n",
      "0.3 0.6964285714285714\n",
      "0.4 0.7222222222222222\n",
      "0.5 0.7540983606557377\n",
      "0.6 0.7884615384615384\n",
      "0.7 0.8181818181818182\n",
      "0.8 0.8490566037735849\n",
      "0.9 0.9019607843137255\n",
      "0.95 0.9375\n",
      "CHEMBL3755859\n",
      "0.05 0.45614035087719296\n",
      "0.1 0.5\n",
      "0.2 0.5581395348837209\n",
      "0.3 0.5853658536585366\n",
      "0.4 0.625\n",
      "0.5 0.675\n",
      "0.6 0.7631578947368421\n",
      "0.7 0.8108108108108109\n",
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      "0.9 0.9117647058823529\n",
      "0.95 0.9722222222222222\n",
      "CHEMBL3707859\n",
      "0.05 0.6164383561643836\n",
      "0.1 0.6438356164383562\n",
      "0.2 0.71875\n",
      "0.3 0.7692307692307693\n",
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      "0.8 0.875\n",
      "0.9 0.9056603773584906\n",
      "0.95 0.9259259259259259\n",
      "CHEMBL3705383\n",
      "0.05 0.6164383561643836\n",
      "0.1 0.6438356164383562\n",
      "0.2 0.71875\n",
      "0.3 0.7692307692307693\n",
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      "0.8 0.875\n",
      "0.9 0.9056603773584906\n",
      "0.95 0.9259259259259259\n",
      "CHEMBL3707832\n",
      "0.05 0.6164383561643836\n",
      "0.1 0.6438356164383562\n",
      "0.2 0.71875\n",
      "0.3 0.7692307692307693\n",
      "0.4 0.7931034482758621\n",
      "0.5 0.8135593220338984\n",
      "0.6 0.8333333333333334\n",
      "0.7 0.8571428571428571\n",
      "0.8 0.875\n",
      "0.9 0.9056603773584906\n",
      "0.95 0.9259259259259259\n",
      "CHEMBL894022\n",
      "0.05 0.6\n",
      "0.1 0.6333333333333333\n",
      "0.2 0.6666666666666666\n",
      "0.3 0.6909090909090909\n",
      "0.4 0.7115384615384616\n",
      "0.5 0.7346938775510204\n",
      "0.6 0.7592592592592593\n",
      "0.7 0.7884615384615384\n",
      "0.8 0.8235294117647058\n",
      "0.9 0.8695652173913043\n",
      "0.95 0.9069767441860465\n",
      "CHEMBL949088\n",
      "0.05 0.5254237288135594\n",
      "0.1 0.5636363636363636\n",
      "0.2 0.6037735849056604\n",
      "0.3 0.6326530612244898\n",
      "0.4 0.6595744680851063\n",
      "0.5 0.6888888888888889\n",
      "0.6 0.717948717948718\n",
      "0.7 0.7551020408163265\n",
      "0.8 0.7916666666666666\n",
      "0.9 0.8409090909090909\n",
      "0.95 0.8918918918918919\n",
      "CHEMBL906590\n",
      "0.05 0.5135135135135135\n",
      "0.1 0.5393258426966292\n",
      "0.2 0.573170731707317\n",
      "0.3 0.5974025974025974\n",
      "0.4 0.6216216216216216\n",
      "0.5 0.6486486486486487\n",
      "0.6 0.684931506849315\n",
      "0.7 0.7313432835820896\n",
      "0.8 0.7837837837837838\n",
      "0.9 0.8596491228070176\n",
      "0.95 0.9117647058823529\n",
      "CHEMBL839940\n",
      "0.05 0.5737704918032787\n",
      "0.1 0.6\n",
      "0.2 0.6346153846153846\n",
      "0.3 0.6607142857142857\n",
      "0.4 0.6909090909090909\n",
      "0.5 0.7192982456140351\n",
      "0.6 0.7674418604651163\n",
      "0.7 0.825\n",
      "0.8 0.8974358974358975\n",
      "0.9 0.95\n",
      "0.95 0.9705882352941176\n",
      "CHEMBL828963\n",
      "0.05 0.5645161290322581\n",
      "0.1 0.5833333333333334\n",
      "0.2 0.6206896551724138\n",
      "0.3 0.6481481481481481\n",
      "0.4 0.6785714285714286\n",
      "0.5 0.7058823529411765\n",
      "0.6 0.7346938775510204\n",
      "0.7 0.7692307692307693\n",
      "0.8 0.8103448275862069\n",
      "0.9 0.8688524590163934\n",
      "0.95 0.9074074074074074\n",
      "CHEMBL827377\n",
      "0.05 0.5714285714285714\n",
      "0.1 0.6\n",
      "0.2 0.6346153846153846\n",
      "0.3 0.660377358490566\n",
      "0.4 0.6909090909090909\n",
      "0.5 0.717391304347826\n",
      "0.6 0.7619047619047619\n",
      "0.7 0.825\n",
      "0.8 0.8974358974358975\n",
      "0.9 0.95\n",
      "0.95 0.9736842105263158\n",
      "CHEMBL3707804\n",
      "0.05 0.5942028985507246\n",
      "0.1 0.6268656716417911\n",
      "0.2 0.7096774193548387\n",
      "0.3 0.75\n",
      "0.4 0.7719298245614035\n",
      "0.5 0.8\n",
      "0.6 0.8181818181818182\n",
      "0.7 0.8461538461538461\n",
      "0.8 0.8688524590163934\n",
      "0.9 0.9038461538461539\n",
      "0.95 0.9361702127659575\n",
      "CHEMBL3706283\n",
      "0.05 0.620253164556962\n",
      "0.1 0.6428571428571429\n",
      "0.2 0.676056338028169\n",
      "0.3 0.7101449275362319\n",
      "0.4 0.75\n",
      "0.5 0.78125\n",
      "0.6 0.8181818181818182\n",
      "0.7 0.8548387096774194\n",
      "0.8 0.8870967741935484\n",
      "0.9 0.9354838709677419\n",
      "0.95 0.9705882352941176\n",
      "CHEMBL3734209\n",
      "0.05 0.686046511627907\n",
      "0.1 0.7023809523809523\n",
      "0.2 0.7215189873417721\n",
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      "0.4 0.7625\n",
      "0.5 0.7916666666666666\n",
      "0.6 0.8333333333333334\n",
      "0.7 0.8607594936708861\n",
      "0.8 0.8860759493670886\n",
      "0.9 0.9180327868852459\n",
      "0.95 0.9342105263157895\n",
      "CHEMBL3707598\n",
      "0.05 0.5942028985507246\n",
      "0.1 0.6268656716417911\n",
      "0.2 0.7096774193548387\n",
      "0.3 0.75\n",
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      "0.8 0.8688524590163934\n",
      "0.9 0.9038461538461539\n",
      "0.95 0.9361702127659575\n",
      "CHEMBL821446\n",
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      "0.1 0.6483516483516484\n",
      "0.2 0.6931818181818182\n",
      "0.3 0.75\n",
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      "CHEMBL3706129\n",
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      "0.1 0.6266666666666667\n",
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      "0.8 0.8653846153846154\n",
      "0.9 0.9\n",
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      "CHEMBL840221\n",
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      "CHEMBL3223904\n",
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      "CHEMBL840214\n",
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      "CHEMBL839693\n",
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      "CHEMBL1064501\n",
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      "0.2 0.6530612244897959\n",
      "0.3 0.6956521739130435\n",
      "0.4 0.7380952380952381\n",
      "0.5 0.7777777777777778\n",
      "0.6 0.8\n",
      "0.7 0.8260869565217391\n",
      "0.8 0.8604651162790697\n",
      "0.9 0.9\n",
      "0.95 0.9333333333333333\n",
      "CHEMBL1037716\n",
      "0.05 0.746268656716418\n",
      "0.1 0.7692307692307693\n",
      "0.2 0.8\n",
      "0.3 0.8225806451612904\n",
      "0.4 0.8360655737704918\n",
      "0.5 0.8596491228070176\n",
      "0.6 0.8771929824561403\n",
      "0.7 0.8947368421052632\n",
      "0.8 0.9259259259259259\n",
      "0.9 0.9464285714285714\n",
      "0.95 0.9642857142857143\n",
      "CHEMBL4392265\n",
      "0.05 0.7619047619047619\n",
      "0.1 0.7790697674418605\n",
      "0.2 0.7976190476190477\n",
      "0.3 0.8125\n",
      "0.4 0.8313253012048193\n",
      "0.5 0.8481012658227848\n",
      "0.6 0.8625\n",
      "0.7 0.8831168831168831\n",
      "0.8 0.9041095890410958\n",
      "0.9 0.9324324324324325\n",
      "0.95 0.9518072289156626\n",
      "CHEMBL1068886\n",
      "0.05 0.5892857142857143\n",
      "0.1 0.6181818181818182\n",
      "0.2 0.6530612244897959\n",
      "0.3 0.6956521739130435\n",
      "0.4 0.7380952380952381\n",
      "0.5 0.7777777777777778\n",
      "0.6 0.8\n",
      "0.7 0.8260869565217391\n",
      "0.8 0.8604651162790697\n",
      "0.9 0.9\n",
      "0.95 0.9333333333333333\n",
      "CHEMBL1068887\n",
      "0.05 0.5892857142857143\n",
      "0.1 0.6181818181818182\n",
      "0.2 0.6530612244897959\n",
      "0.3 0.6956521739130435\n",
      "0.4 0.7380952380952381\n",
      "0.5 0.7777777777777778\n",
      "0.6 0.8\n",
      "0.7 0.8260869565217391\n",
      "0.8 0.8604651162790697\n",
      "0.9 0.9\n",
      "0.95 0.9333333333333333\n",
      "CHEMBL2351085\n",
      "0.05 0.6388888888888888\n",
      "0.1 0.6714285714285714\n",
      "0.2 0.7066666666666667\n",
      "0.3 0.7352941176470589\n",
      "0.4 0.7619047619047619\n",
      "0.5 0.7868852459016393\n",
      "0.6 0.8181818181818182\n",
      "0.7 0.8571428571428571\n",
      "0.8 0.8867924528301887\n",
      "0.9 0.9272727272727272\n",
      "0.95 0.9615384615384616\n",
      "CHEMBL4416273\n",
      "0.05 0.6851851851851852\n",
      "0.1 0.723404255319149\n",
      "0.2 0.7735849056603774\n",
      "0.3 0.84\n",
      "0.4 0.8936170212765957\n",
      "0.5 0.9130434782608695\n",
      "0.6 0.9148936170212766\n",
      "0.7 0.9347826086956522\n",
      "0.8 0.9565217391304348\n",
      "0.9 1.0\n",
      "0.95 1.0\n",
      "CHEMBL3073105\n",
      "0.05 0.5205479452054794\n",
      "0.1 0.5625\n",
      "0.2 0.6031746031746031\n",
      "0.3 0.6461538461538462\n",
      "0.4 0.6909090909090909\n",
      "0.5 0.7258064516129032\n",
      "0.6 0.75\n",
      "0.7 0.7777777777777778\n",
      "0.8 0.8142857142857143\n",
      "0.9 0.8709677419354839\n",
      "0.95 0.9090909090909091\n",
      "CHEMBL4262345\n",
      "0.05 0.6212121212121212\n",
      "0.1 0.6567164179104478\n",
      "0.2 0.7121212121212122\n",
      "0.3 0.7424242424242424\n",
      "0.4 0.7619047619047619\n",
      "0.5 0.7903225806451613\n",
      "0.6 0.8135593220338984\n",
      "0.7 0.8412698412698413\n",
      "0.8 0.875\n",
      "0.9 0.9137931034482759\n",
      "0.95 0.9482758620689655\n",
      "CHEMBL4262391\n",
      "0.05 0.6666666666666666\n",
      "0.1 0.7068965517241379\n",
      "0.2 0.7454545454545455\n",
      "0.3 0.7843137254901961\n",
      "0.4 0.803921568627451\n",
      "0.5 0.8333333333333334\n",
      "0.6 0.851063829787234\n",
      "0.7 0.875\n",
      "0.8 0.9090909090909091\n",
      "0.9 0.9361702127659575\n",
      "0.95 0.9583333333333334\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMACCSKeysFingerprint(x),\"MACCS\",accum)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Morgan FPs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHEMBL3706284\n",
      "0.05 0.6216216216216216\n",
      "0.1 0.6486486486486487\n",
      "0.2 0.6842105263157895\n",
      "0.3 0.7105263157894737\n",
      "0.4 0.7352941176470589\n",
      "0.5 0.7647058823529411\n",
      "0.6 0.7837837837837838\n",
      "0.7 0.8181818181818182\n",
      "0.8 0.8529411764705882\n",
      "0.9 0.9090909090909091\n",
      "0.95 0.9393939393939394\n",
      "CHEMBL3705289\n",
      "0.05 0.5\n",
      "0.1 0.5384615384615384\n",
      "0.2 0.5714285714285714\n",
      "0.3 0.6086956521739131\n",
      "0.4 0.6363636363636364\n",
      "0.5 0.6666666666666666\n",
      "0.6 0.7\n",
      "0.7 0.7368421052631579\n",
      "0.8 0.7894736842105263\n",
      "0.9 0.8421052631578947\n",
      "0.95 0.8888888888888888\n",
      "CHEMBL3734283\n",
      "0.05 0.5294117647058824\n",
      "0.1 0.5625\n",
      "0.2 0.59375\n",
      "0.3 0.6206896551724138\n",
      "0.4 0.6451612903225806\n",
      "0.5 0.6774193548387096\n",
      "0.6 0.7\n",
      "0.7 0.7333333333333333\n",
      "0.8 0.78125\n",
      "0.9 0.84\n",
      "0.95 0.88\n",
      "CHEMBL3755859\n",
      "0.05 0.425\n",
      "0.1 0.4594594594594595\n",
      "0.2 0.5\n",
      "0.3 0.5357142857142857\n",
      "0.4 0.5675675675675675\n",
      "0.5 0.6\n",
      "0.6 0.6296296296296297\n",
      "0.7 0.6666666666666666\n",
      "0.8 0.7096774193548387\n",
      "0.9 0.7741935483870968\n",
      "0.95 0.84\n",
      "CHEMBL3707859\n",
      "0.05 0.6097560975609756\n",
      "0.1 0.631578947368421\n",
      "0.2 0.6578947368421053\n",
      "0.3 0.6842105263157895\n",
      "0.4 0.7058823529411765\n",
      "0.5 0.7297297297297297\n",
      "0.6 0.75\n",
      "0.7 0.775\n",
      "0.8 0.8125\n",
      "0.9 0.8648648648648649\n",
      "0.95 0.90625\n",
      "CHEMBL3705383\n",
      "0.05 0.6097560975609756\n",
      "0.1 0.631578947368421\n",
      "0.2 0.6578947368421053\n",
      "0.3 0.6842105263157895\n",
      "0.4 0.7058823529411765\n",
      "0.5 0.7297297297297297\n",
      "0.6 0.75\n",
      "0.7 0.775\n",
      "0.8 0.8125\n",
      "0.9 0.8648648648648649\n",
      "0.95 0.90625\n",
      "CHEMBL3707832\n",
      "0.05 0.6097560975609756\n",
      "0.1 0.631578947368421\n",
      "0.2 0.6578947368421053\n",
      "0.3 0.6842105263157895\n",
      "0.4 0.7058823529411765\n",
      "0.5 0.7297297297297297\n",
      "0.6 0.75\n",
      "0.7 0.775\n",
      "0.8 0.8125\n",
      "0.9 0.8648648648648649\n",
      "0.95 0.90625\n",
      "CHEMBL894022\n",
      "0.05 0.4482758620689655\n",
      "0.1 0.5\n",
      "0.2 0.5483870967741935\n",
      "0.3 0.5925925925925926\n",
      "0.4 0.6333333333333333\n",
      "0.5 0.6666666666666666\n",
      "0.6 0.7037037037037037\n",
      "0.7 0.7391304347826086\n",
      "0.8 0.7777777777777778\n",
      "0.9 0.8333333333333334\n",
      "0.95 0.875\n",
      "CHEMBL949088\n",
      "0.05 0.4634146341463415\n",
      "0.1 0.5227272727272727\n",
      "0.2 0.5882352941176471\n",
      "0.3 0.6341463414634146\n",
      "0.4 0.6571428571428571\n",
      "0.5 0.6875\n",
      "0.6 0.7241379310344828\n",
      "0.7 0.7692307692307693\n",
      "0.8 0.8125\n",
      "0.9 0.8666666666666667\n",
      "0.95 0.9090909090909091\n",
      "CHEMBL906590\n",
      "0.05 0.5\n",
      "0.1 0.5319148936170213\n",
      "0.2 0.5652173913043478\n",
      "0.3 0.5918367346938775\n",
      "0.4 0.6222222222222222\n",
      "0.5 0.6521739130434783\n",
      "0.6 0.6904761904761905\n",
      "0.7 0.7368421052631579\n",
      "0.8 0.7837837837837838\n",
      "0.9 0.8461538461538461\n",
      "0.95 0.8947368421052632\n",
      "CHEMBL839940\n",
      "0.05 0.5641025641025641\n",
      "0.1 0.5897435897435898\n",
      "0.2 0.6190476190476191\n",
      "0.3 0.6470588235294118\n",
      "0.4 0.6764705882352942\n",
      "0.5 0.7\n",
      "0.6 0.7297297297297297\n",
      "0.7 0.7631578947368421\n",
      "0.8 0.8\n",
      "0.9 0.8571428571428571\n",
      "0.95 0.9090909090909091\n",
      "CHEMBL828963\n",
      "0.05 0.4791666666666667\n",
      "0.1 0.5111111111111111\n",
      "0.2 0.5476190476190477\n",
      "0.3 0.5853658536585366\n",
      "0.4 0.6153846153846154\n",
      "0.5 0.6511627906976745\n",
      "0.6 0.6904761904761905\n",
      "0.7 0.7297297297297297\n",
      "0.8 0.775\n",
      "0.9 0.8378378378378378\n",
      "0.95 0.8717948717948718\n",
      "CHEMBL827377\n",
      "0.05 0.5641025641025641\n",
      "0.1 0.5882352941176471\n",
      "0.2 0.6176470588235294\n",
      "0.3 0.6470588235294118\n",
      "0.4 0.6764705882352942\n",
      "0.5 0.7\n",
      "0.6 0.7297297297297297\n",
      "0.7 0.7631578947368421\n",
      "0.8 0.8\n",
      "0.9 0.8571428571428571\n",
      "0.95 0.9090909090909091\n",
      "CHEMBL3707804\n",
      "0.05 0.6046511627906976\n",
      "0.1 0.6190476190476191\n",
      "0.2 0.65\n",
      "0.3 0.675\n",
      "0.4 0.7027027027027027\n",
      "0.5 0.7272727272727273\n",
      "0.6 0.7560975609756098\n",
      "0.7 0.78125\n",
      "0.8 0.8157894736842105\n",
      "0.9 0.8611111111111112\n",
      "0.95 0.9032258064516129\n",
      "CHEMBL3706283\n",
      "0.05 0.6216216216216216\n",
      "0.1 0.6486486486486487\n",
      "0.2 0.6764705882352942\n",
      "0.3 0.7058823529411765\n",
      "0.4 0.7297297297297297\n",
      "0.5 0.7575757575757576\n",
      "0.6 0.78125\n",
      "0.7 0.8181818181818182\n",
      "0.8 0.8529411764705882\n",
      "0.9 0.9090909090909091\n",
      "0.95 0.9393939393939394\n",
      "CHEMBL3734209\n",
      "0.05 0.5652173913043478\n",
      "0.1 0.6170212765957447\n",
      "0.2 0.6666666666666666\n",
      "0.3 0.6976744186046512\n",
      "0.4 0.725\n",
      "0.5 0.7446808510638298\n",
      "0.6 0.7674418604651163\n",
      "0.7 0.7948717948717948\n",
      "0.8 0.825\n",
      "0.9 0.8536585365853658\n",
      "0.95 0.8918918918918919\n",
      "CHEMBL3707598\n",
      "0.05 0.6046511627906976\n",
      "0.1 0.6190476190476191\n",
      "0.2 0.65\n",
      "0.3 0.675\n",
      "0.4 0.7027027027027027\n",
      "0.5 0.7272727272727273\n",
      "0.6 0.7560975609756098\n",
      "0.7 0.78125\n",
      "0.8 0.8157894736842105\n",
      "0.9 0.8611111111111112\n",
      "0.95 0.9032258064516129\n",
      "CHEMBL821446\n",
      "0.05 0.4666666666666667\n",
      "0.1 0.5\n",
      "0.2 0.56\n",
      "0.3 0.6041666666666666\n",
      "0.4 0.6458333333333334\n",
      "0.5 0.6829268292682927\n",
      "0.6 0.725\n",
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      "0.8 0.8043478260869565\n",
      "0.9 0.8648648648648649\n",
      "0.95 0.90625\n",
      "CHEMBL3706129\n",
      "0.05 0.6046511627906976\n",
      "0.1 0.6190476190476191\n",
      "0.2 0.65\n",
      "0.3 0.675\n",
      "0.4 0.7027027027027027\n",
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      "0.6 0.7560975609756098\n",
      "0.7 0.78125\n",
      "0.8 0.8157894736842105\n",
      "0.9 0.8620689655172413\n",
      "0.95 0.9032258064516129\n",
      "CHEMBL840221\n",
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      "0.1 0.47619047619047616\n",
      "0.2 0.5384615384615384\n",
      "0.3 0.5833333333333334\n",
      "0.4 0.6216216216216216\n",
      "0.5 0.6578947368421053\n",
      "0.6 0.6842105263157895\n",
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      "0.8 0.7647058823529411\n",
      "0.9 0.8333333333333334\n",
      "0.95 0.8857142857142857\n",
      "CHEMBL3223904\n",
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      "0.1 0.6\n",
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      "0.3 0.6666666666666666\n",
      "0.4 0.6896551724137931\n",
      "0.5 0.7142857142857143\n",
      "0.6 0.7407407407407407\n",
      "0.7 0.7692307692307693\n",
      "0.8 0.8\n",
      "0.9 0.84\n",
      "0.95 0.875\n",
      "CHEMBL840214\n",
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      "0.1 0.4772727272727273\n",
      "0.2 0.5434782608695652\n",
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      "0.6 0.6829268292682927\n",
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      "0.8 0.7619047619047619\n",
      "0.9 0.8372093023255814\n",
      "0.95 0.8888888888888888\n",
      "CHEMBL839693\n",
      "0.05 0.6444444444444445\n",
      "0.1 0.6595744680851063\n",
      "0.2 0.7045454545454546\n",
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      "0.4 0.7631578947368421\n",
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      "0.6 0.8292682926829268\n",
      "0.7 0.8571428571428571\n",
      "0.8 0.8918918918918919\n",
      "0.9 0.9230769230769231\n",
      "0.95 0.9459459459459459\n",
      "CHEMBL1064501\n",
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      "0.1 0.7962962962962963\n",
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      "CHEMBL894050\n",
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      "0.1 0.30434782608695654\n",
      "0.2 0.36\n",
      "0.3 0.4\n",
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      "0.6 0.5238095238095238\n",
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      "0.8 0.6521739130434783\n",
      "0.9 0.75\n",
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      "CHEMBL3297241\n",
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      "0.7 0.8235294117647058\n",
      "0.8 0.84375\n",
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      "CHEMBL3297242\n",
      "0.05 0.6486486486486487\n",
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      "0.6 0.8\n",
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      "CHEMBL3297243\n",
      "0.05 0.6486486486486487\n",
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      "0.7 0.8235294117647058\n",
      "0.8 0.84375\n",
      "0.9 0.875\n",
      "0.95 0.9090909090909091\n",
      "CHEMBL3297244\n",
      "0.05 0.6486486486486487\n",
      "0.1 0.6756756756756757\n",
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      "0.2 0.6333333333333333\n",
      "0.3 0.6896551724137931\n",
      "0.4 0.7407407407407407\n",
      "0.5 0.7692307692307693\n",
      "0.6 0.7931034482758621\n",
      "0.7 0.8214285714285714\n",
      "0.8 0.8518518518518519\n",
      "0.9 0.8846153846153846\n",
      "0.95 0.9166666666666666\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,0),\"Morgan0 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,1),\"Morgan1 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,2),\"Morgan2 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL4262391\n",
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    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,3),\"Morgan3 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bit-vector based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL3705289\n",
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   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,0,1024),\"Morgan0 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL4392265\n",
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      "CHEMBL3073105\n",
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      "CHEMBL4262345\n",
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      "0.2 0.5068493150684932\n",
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      "0.5 0.6\n",
      "0.6 0.6363636363636364\n",
      "0.7 0.6716417910447762\n",
      "0.8 0.7096774193548387\n",
      "0.9 0.8148148148148148\n",
      "0.95 0.8846153846153846\n",
      "CHEMBL4262391\n",
      "0.05 0.4864864864864865\n",
      "0.1 0.5238095238095238\n",
      "0.2 0.559322033898305\n",
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      "0.4 0.6451612903225806\n",
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      "0.6 0.6981132075471698\n",
      "0.7 0.7192982456140351\n",
      "0.8 0.7454545454545455\n",
      "0.9 0.7818181818181819\n",
      "0.95 0.8148148148148148\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,1,1024),\"Morgan1 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL3073105\n",
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      "CHEMBL4262345\n",
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      "CHEMBL4262391\n",
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      "0.9 0.7126436781609196\n",
      "0.95 0.7402597402597403\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,2,1024),\"Morgan2 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL909851\n",
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      "CHEMBL941996\n",
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      "0.95 0.7844827586206896\n",
      "CHEMBL1068888\n",
      "0.05 0.22784810126582278\n",
      "0.1 0.31958762886597936\n",
      "0.2 0.3473684210526316\n",
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      "0.8 0.6235294117647059\n",
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      "0.95 0.75\n",
      "CHEMBL1037716\n",
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      "0.1 0.4573170731707317\n",
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      "0.95 0.7724550898203593\n",
      "CHEMBL4392265\n",
      "0.05 0.2781954887218045\n",
      "0.1 0.31297709923664124\n",
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      "0.3 0.39285714285714285\n",
      "0.4 0.4316546762589928\n",
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      "0.6 0.5\n",
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      "0.8 0.5673076923076923\n",
      "0.9 0.6190476190476191\n",
      "0.95 0.6804123711340206\n",
      "CHEMBL1068886\n",
      "0.05 0.22784810126582278\n",
      "0.1 0.31958762886597936\n",
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      "0.7 0.5714285714285714\n",
      "0.8 0.6235294117647059\n",
      "0.9 0.6901408450704225\n",
      "0.95 0.75\n",
      "CHEMBL1068887\n",
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      "0.1 0.31958762886597936\n",
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      "0.8 0.6235294117647059\n",
      "0.9 0.6901408450704225\n",
      "0.95 0.75\n",
      "CHEMBL2351085\n",
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      "0.1 0.32413793103448274\n",
      "0.2 0.3597122302158273\n",
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      "0.4 0.46923076923076923\n",
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      "0.7 0.5772357723577236\n",
      "0.8 0.631578947368421\n",
      "0.9 0.6909090909090909\n",
      "0.95 0.7425742574257426\n",
      "CHEMBL4416273\n",
      "0.05 0.37037037037037035\n",
      "0.1 0.39552238805970147\n",
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      "0.4 0.496\n",
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      "0.7 0.5892857142857143\n",
      "0.8 0.6306306306306306\n",
      "0.9 0.6846846846846847\n",
      "0.95 0.7314814814814815\n",
      "CHEMBL3073105\n",
      "0.05 0.2722222222222222\n",
      "0.1 0.2838709677419355\n",
      "0.2 0.3\n",
      "0.3 0.3150684931506849\n",
      "0.4 0.33121019108280253\n",
      "0.5 0.35185185185185186\n",
      "0.6 0.3972602739726027\n",
      "0.7 0.44696969696969696\n",
      "0.8 0.5038167938931297\n",
      "0.9 0.6666666666666666\n",
      "0.95 0.7105263157894737\n",
      "CHEMBL4262345\n",
      "0.05 0.28\n",
      "0.1 0.32894736842105265\n",
      "0.2 0.40540540540540543\n",
      "0.3 0.4645669291338583\n",
      "0.4 0.5074626865671642\n",
      "0.5 0.5348837209302325\n",
      "0.6 0.5652173913043478\n",
      "0.7 0.5984251968503937\n",
      "0.8 0.6386554621848739\n",
      "0.9 0.6981132075471698\n",
      "0.95 0.7570093457943925\n",
      "CHEMBL4262391\n",
      "0.05 0.4253731343283582\n",
      "0.1 0.45528455284552843\n",
      "0.2 0.4793388429752066\n",
      "0.3 0.5163934426229508\n",
      "0.4 0.5565217391304348\n",
      "0.5 0.5818181818181818\n",
      "0.6 0.5982142857142857\n",
      "0.7 0.6153846153846154\n",
      "0.8 0.6379310344827587\n",
      "0.9 0.6695652173913044\n",
      "0.95 0.6915887850467289\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,3,1024),\"Morgan3 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FeatMorgan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.9 0.9375\n",
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      "CHEMBL3705289\n",
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      "CHEMBL906590\n",
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      "CHEMBL941996\n",
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      "CHEMBL4392265\n"
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    {
     "name": "stdout",
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     "text": [
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      "CHEMBL4416273\n",
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      "0.8 0.9629629629629629\n",
      "0.9 1.0\n",
      "0.95 1.0\n",
      "CHEMBL3073105\n",
      "0.05 0.65625\n",
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      "CHEMBL4262345\n",
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      "0.1 0.5862068965517241\n",
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      "0.8 0.9230769230769231\n",
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      "CHEMBL4262391\n",
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      "0.1 0.6\n",
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      "0.3 0.7407407407407407\n",
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      "0.8 0.8846153846153846\n",
      "0.9 0.9230769230769231\n",
      "0.95 0.96\n"
     ]
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   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,0,useFeatures=True),\"FeatMorgan0 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL1037716\n",
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      "CHEMBL4392265\n",
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      "CHEMBL1068886\n",
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      "CHEMBL1068887\n",
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      "0.6 0.7837837837837838\n",
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "0.8 0.9259259259259259\n",
      "0.9 0.9629629629629629\n",
      "0.95 1.0\n",
      "CHEMBL3073105\n",
      "0.05 0.5211267605633803\n",
      "0.1 0.5492957746478874\n",
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      "0.4 0.6363636363636364\n",
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      "0.8 0.8064516129032258\n",
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      "CHEMBL4262345\n",
      "0.05 0.48148148148148145\n",
      "0.1 0.5362318840579711\n",
      "0.2 0.5942028985507246\n",
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      "0.4 0.6764705882352942\n",
      "0.5 0.7101449275362319\n",
      "0.6 0.7407407407407407\n",
      "0.7 0.78125\n",
      "0.8 0.8148148148148148\n",
      "0.9 0.8571428571428571\n",
      "0.95 0.9076923076923077\n",
      "CHEMBL4262391\n",
      "0.05 0.5\n",
      "0.1 0.5409836065573771\n",
      "0.2 0.5692307692307692\n",
      "0.3 0.639344262295082\n",
      "0.4 0.6842105263157895\n",
      "0.5 0.7241379310344828\n",
      "0.6 0.75\n",
      "0.7 0.7777777777777778\n",
      "0.8 0.8148148148148148\n",
      "0.9 0.8518518518518519\n",
      "0.95 0.9148936170212766\n"
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    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,1,useFeatures=True),\"FeatMorgan1 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL4392265\n",
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "CHEMBL3073105\n",
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      "0.1 0.42727272727272725\n",
      "0.2 0.4563106796116505\n",
      "0.3 0.48514851485148514\n",
      "0.4 0.5087719298245614\n",
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      "0.95 0.8658536585365854\n",
      "CHEMBL4262345\n",
      "0.05 0.408\n",
      "0.1 0.4807692307692308\n",
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      "0.4 0.6\n",
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      "0.6 0.65625\n",
      "0.7 0.6979166666666666\n",
      "0.8 0.7407407407407407\n",
      "0.9 0.797752808988764\n",
      "0.95 0.8636363636363636\n",
      "CHEMBL4262391\n",
      "0.05 0.47474747474747475\n",
      "0.1 0.5053763440860215\n",
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      "0.4 0.625\n",
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      "0.6 0.686046511627907\n",
      "0.7 0.7093023255813954\n",
      "0.8 0.7341772151898734\n",
      "0.9 0.7901234567901234\n",
      "0.95 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,2,useFeatures=True),\"FeatMorgan2 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.5 0.38613861386138615\n",
      "0.6 0.4316546762589928\n",
      "0.7 0.49\n",
      "0.8 0.5377358490566038\n",
      "0.9 0.6285714285714286\n",
      "0.95 0.735632183908046\n",
      "CHEMBL4334866\n",
      "0.05 0.16363636363636364\n",
      "0.1 0.22857142857142856\n",
      "0.2 0.272108843537415\n",
      "0.3 0.3108108108108108\n",
      "0.4 0.3548387096774194\n",
      "0.5 0.38613861386138615\n",
      "0.6 0.4316546762589928\n",
      "0.7 0.49\n",
      "0.8 0.5377358490566038\n",
      "0.9 0.6285714285714286\n",
      "0.95 0.735632183908046\n",
      "CHEMBL1070310\n",
      "0.05 0.6305732484076433\n",
      "0.1 0.6493506493506493\n",
      "0.2 0.668918918918919\n",
      "0.3 0.684931506849315\n",
      "0.4 0.7092198581560284\n",
      "0.5 0.7328767123287672\n",
      "0.6 0.7586206896551724\n",
      "0.7 0.7857142857142857\n",
      "0.8 0.8181818181818182\n",
      "0.9 0.8515625\n",
      "0.95 0.8666666666666667\n",
      "CHEMBL859414\n",
      "0.05 0.15151515151515152\n",
      "0.1 0.18292682926829268\n",
      "0.2 0.23863636363636365\n",
      "0.3 0.2876712328767123\n",
      "0.4 0.32857142857142857\n",
      "0.5 0.36764705882352944\n",
      "0.6 0.4339622641509434\n",
      "0.7 0.5066666666666667\n",
      "0.8 0.5675675675675675\n",
      "0.9 0.6363636363636364\n",
      "0.95 0.7014925373134329\n",
      "CHEMBL3796307\n",
      "0.05 0.42063492063492064\n",
      "0.1 0.45112781954887216\n",
      "0.2 0.504424778761062\n",
      "0.3 0.5378151260504201\n",
      "0.4 0.5634920634920635\n",
      "0.5 0.5877192982456141\n",
      "0.6 0.6238532110091743\n",
      "0.7 0.6542056074766355\n",
      "0.8 0.696078431372549\n",
      "0.9 0.7575757575757576\n",
      "0.95 0.8152173913043478\n",
      "CHEMBL650169\n",
      "0.05 0.3617021276595745\n",
      "0.1 0.41089108910891087\n",
      "0.2 0.44324324324324327\n",
      "0.3 0.47593582887700536\n",
      "0.4 0.5088757396449705\n",
      "0.5 0.5523809523809524\n",
      "0.6 0.5879396984924623\n",
      "0.7 0.6224489795918368\n",
      "0.8 0.6685082872928176\n",
      "0.9 0.7368421052631579\n",
      "0.95 0.7878787878787878\n",
      "CHEMBL909851\n",
      "0.05 0.2465753424657534\n",
      "0.1 0.3782051282051282\n",
      "0.2 0.4444444444444444\n",
      "0.3 0.47586206896551725\n",
      "0.4 0.5\n",
      "0.5 0.536\n",
      "0.6 0.5772357723577236\n",
      "0.7 0.6238532110091743\n",
      "0.8 0.672566371681416\n",
      "0.9 0.7297297297297297\n",
      "0.95 0.7727272727272727\n",
      "CHEMBL941996\n",
      "0.05 0.313953488372093\n",
      "0.1 0.379746835443038\n",
      "0.2 0.48201438848920863\n",
      "0.3 0.546875\n",
      "0.4 0.5915492957746479\n",
      "0.5 0.6299212598425197\n",
      "0.6 0.6666666666666666\n",
      "0.7 0.7112676056338029\n",
      "0.8 0.7481481481481481\n",
      "0.9 0.796875\n",
      "0.95 0.8264462809917356\n",
      "CHEMBL1068888\n",
      "0.05 0.30275229357798167\n",
      "0.1 0.39622641509433965\n",
      "0.2 0.4536082474226804\n",
      "0.3 0.5\n",
      "0.4 0.5333333333333333\n",
      "0.5 0.5675675675675675\n",
      "0.6 0.5909090909090909\n",
      "0.7 0.6547619047619048\n",
      "0.8 0.7164179104477612\n",
      "0.9 0.796875\n",
      "0.95 0.8311688311688312\n",
      "CHEMBL1037716\n",
      "0.05 0.4942528735632184\n",
      "0.1 0.521978021978022\n",
      "0.2 0.554140127388535\n",
      "0.3 0.5837837837837838\n",
      "0.4 0.6052631578947368\n",
      "0.5 0.6265060240963856\n",
      "0.6 0.6470588235294118\n",
      "0.7 0.6687898089171974\n",
      "0.8 0.7048192771084337\n",
      "0.9 0.7687074829931972\n",
      "0.95 0.8189655172413793\n",
      "CHEMBL4392265\n",
      "0.05 0.32051282051282054\n",
      "0.1 0.36879432624113473\n",
      "0.2 0.4014084507042254\n",
      "0.3 0.4461538461538462\n",
      "0.4 0.47413793103448276\n",
      "0.5 0.5045045045045045\n",
      "0.6 0.5405405405405406\n",
      "0.7 0.5818181818181818\n",
      "0.8 0.6138613861386139\n",
      "0.9 0.6964285714285714\n",
      "0.95 0.7659574468085106\n",
      "CHEMBL1068886\n",
      "0.05 0.30275229357798167\n",
      "0.1 0.39622641509433965\n",
      "0.2 0.4536082474226804\n",
      "0.3 0.5\n",
      "0.4 0.5333333333333333\n",
      "0.5 0.5675675675675675\n",
      "0.6 0.5909090909090909\n",
      "0.7 0.6547619047619048\n",
      "0.8 0.7164179104477612\n",
      "0.9 0.796875\n",
      "0.95 0.8311688311688312\n",
      "CHEMBL1068887\n",
      "0.05 0.30275229357798167\n",
      "0.1 0.39622641509433965\n",
      "0.2 0.4536082474226804\n",
      "0.3 0.5\n",
      "0.4 0.5333333333333333\n",
      "0.5 0.5675675675675675\n",
      "0.6 0.5909090909090909\n",
      "0.7 0.6547619047619048\n",
      "0.8 0.7164179104477612\n",
      "0.9 0.796875\n",
      "0.95 0.8311688311688312\n",
      "CHEMBL2351085\n",
      "0.05 0.35507246376811596\n",
      "0.1 0.3893129770992366\n",
      "0.2 0.42857142857142855\n",
      "0.3 0.4846153846153846\n",
      "0.4 0.5461538461538461\n",
      "0.5 0.6129032258064516\n",
      "0.6 0.6517857142857143\n",
      "0.7 0.6859504132231405\n",
      "0.8 0.7155963302752294\n",
      "0.9 0.7850467289719626\n",
      "0.95 0.8207547169811321\n",
      "CHEMBL4416273\n",
      "0.05 0.5163934426229508\n",
      "0.1 0.5483870967741935\n",
      "0.2 0.5833333333333334\n",
      "0.3 0.6120689655172413\n",
      "0.4 0.6347826086956522\n",
      "0.5 0.6636363636363637\n",
      "0.6 0.7037037037037037\n",
      "0.7 0.7358490566037735\n",
      "0.8 0.7692307692307693\n",
      "0.9 0.81\n",
      "0.95 0.8367346938775511\n",
      "CHEMBL3073105\n",
      "0.05 0.2953020134228188\n",
      "0.1 0.3236994219653179\n",
      "0.2 0.34810126582278483\n",
      "0.3 0.3724137931034483\n",
      "0.4 0.3900709219858156\n",
      "0.5 0.4148148148148148\n",
      "0.6 0.47058823529411764\n",
      "0.7 0.5170068027210885\n",
      "0.8 0.5753424657534246\n",
      "0.9 0.7457627118644068\n",
      "0.95 0.8181818181818182\n",
      "CHEMBL4262345\n",
      "0.05 0.3313253012048193\n",
      "0.1 0.4153846153846154\n",
      "0.2 0.4689655172413793\n",
      "0.3 0.5066666666666667\n",
      "0.4 0.5348837209302325\n",
      "0.5 0.5658914728682171\n",
      "0.6 0.59375\n",
      "0.7 0.6330275229357798\n",
      "0.8 0.6730769230769231\n",
      "0.9 0.75\n",
      "0.95 0.8269230769230769\n",
      "CHEMBL4262391\n",
      "0.05 0.4318181818181818\n",
      "0.1 0.45901639344262296\n",
      "0.2 0.4788732394366197\n",
      "0.3 0.5112781954887218\n",
      "0.4 0.5775862068965517\n",
      "0.5 0.6146788990825688\n",
      "0.6 0.6396396396396397\n",
      "0.7 0.6608695652173913\n",
      "0.8 0.6893203883495146\n",
      "0.9 0.7358490566037735\n",
      "0.95 0.780952380952381\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetMorganFingerprint(x,3,useFeatures=True),\"FeatMorgan3 (counts)\",accum)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bit vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.05 0.6764705882352942\n",
      "0.1 0.7058823529411765\n",
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      "0.3 0.7647058823529411\n",
      "0.4 0.7941176470588235\n",
      "0.5 0.8181818181818182\n",
      "0.6 0.84375\n",
      "0.7 0.875\n",
      "0.8 0.9032258064516129\n",
      "0.9 0.9375\n",
      "0.95 0.9655172413793104\n",
      "CHEMBL3705289\n",
      "0.05 0.5769230769230769\n",
      "0.1 0.625\n",
      "0.2 0.7142857142857143\n",
      "0.3 0.7619047619047619\n",
      "0.4 0.8\n",
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      "0.6 0.8421052631578947\n",
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      "0.8 0.8947368421052632\n",
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      "CHEMBL3734283\n",
      "0.05 0.625\n",
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      "CHEMBL3755859\n",
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      "CHEMBL3707859\n",
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      "CHEMBL3705383\n",
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      "CHEMBL906590\n",
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      "CHEMBL839940\n",
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      "CHEMBL828963\n",
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      "CHEMBL827377\n",
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      "CHEMBL3707804\n",
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      "CHEMBL3706283\n",
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      "CHEMBL3734209\n",
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      "CHEMBL3707598\n",
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      "0.8 0.90625\n",
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      "CHEMBL821446\n",
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      "CHEMBL840221\n",
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      "0.95 1.0\n",
      "CHEMBL1037716\n",
      "0.05 0.6521739130434783\n",
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      "0.95 0.9714285714285714\n",
      "CHEMBL4392265\n",
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      "0.1 0.6470588235294118\n",
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      "0.95 0.9615384615384616\n",
      "CHEMBL1068886\n",
      "0.05 0.5789473684210527\n",
      "0.1 0.6086956521739131\n",
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      "0.6 0.85\n",
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      "0.95 1.0\n",
      "CHEMBL1068887\n",
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      "0.1 0.6086956521739131\n",
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      "0.4 0.7727272727272727\n",
      "0.5 0.8181818181818182\n",
      "0.6 0.85\n",
      "0.7 0.8947368421052632\n",
      "0.8 0.9444444444444444\n",
      "0.9 0.9523809523809523\n",
      "0.95 1.0\n",
      "CHEMBL2351085\n",
      "0.05 0.7\n",
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      "0.95 0.9642857142857143\n",
      "CHEMBL4416273\n",
      "0.05 0.8214285714285714\n",
      "0.1 0.8571428571428571\n",
      "0.2 0.8888888888888888\n",
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      "0.7 0.9615384615384616\n",
      "0.8 0.9629629629629629\n",
      "0.9 1.0\n",
      "0.95 1.0\n",
      "CHEMBL3073105\n",
      "0.05 0.65625\n",
      "0.1 0.6764705882352942\n",
      "0.2 0.696969696969697\n",
      "0.3 0.71875\n",
      "0.4 0.75\n",
      "0.5 0.78125\n",
      "0.6 0.8064516129032258\n",
      "0.7 0.8571428571428571\n",
      "0.8 0.8928571428571429\n",
      "0.9 0.9259259259259259\n",
      "0.95 0.9428571428571428\n",
      "CHEMBL4262345\n",
      "0.05 0.525\n",
      "0.1 0.5862068965517241\n",
      "0.2 0.65625\n",
      "0.3 0.7419354838709677\n",
      "0.4 0.7931034482758621\n",
      "0.5 0.8214285714285714\n",
      "0.6 0.8518518518518519\n",
      "0.7 0.8888888888888888\n",
      "0.8 0.9230769230769231\n",
      "0.9 0.9583333333333334\n",
      "0.95 0.9615384615384616\n",
      "CHEMBL4262391\n",
      "0.05 0.5666666666666667\n",
      "0.1 0.6\n",
      "0.2 0.6551724137931034\n",
      "0.3 0.7407407407407407\n",
      "0.4 0.7777777777777778\n",
      "0.5 0.8148148148148148\n",
      "0.6 0.8461538461538461\n",
      "0.7 0.875\n",
      "0.8 0.8846153846153846\n",
      "0.9 0.9230769230769231\n",
      "0.95 0.96\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,0,1024,useFeatures=True),\"FeatMorgan0 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL1068886\n",
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      "CHEMBL1068887\n",
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      "0.7 0.813953488372093\n",
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "0.9 0.9629629629629629\n",
      "0.95 1.0\n",
      "CHEMBL3073105\n",
      "0.05 0.525\n",
      "0.1 0.5492957746478874\n",
      "0.2 0.5844155844155844\n",
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      "CHEMBL4262345\n",
      "0.05 0.48148148148148145\n",
      "0.1 0.5362318840579711\n",
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      "0.5 0.7101449275362319\n",
      "0.6 0.7407407407407407\n",
      "0.7 0.78125\n",
      "0.8 0.8148148148148148\n",
      "0.9 0.8571428571428571\n",
      "0.95 0.9076923076923077\n",
      "CHEMBL4262391\n",
      "0.05 0.5\n",
      "0.1 0.5409836065573771\n",
      "0.2 0.5692307692307692\n",
      "0.3 0.639344262295082\n",
      "0.4 0.6842105263157895\n",
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      "0.6 0.7543859649122807\n",
      "0.7 0.7777777777777778\n",
      "0.8 0.8148148148148148\n",
      "0.9 0.8518518518518519\n",
      "0.95 0.9148936170212766\n"
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    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,1,1024,useFeatures=True),\"FeatMorgan1 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "CHEMBL3073105\n",
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      "CHEMBL4262345\n",
      "0.05 0.4098360655737705\n",
      "0.1 0.49\n",
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      "0.8 0.7411764705882353\n",
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      "CHEMBL4262391\n",
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      "0.7 0.7108433734939759\n",
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      "0.95 0.8333333333333334\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,2,1024,useFeatures=True),\"FeatMorgan2 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.8 0.7083333333333334\n",
      "0.9 0.7687074829931972\n",
      "0.95 0.8189655172413793\n",
      "CHEMBL4392265\n",
      "0.05 0.33121019108280253\n",
      "0.1 0.3769230769230769\n",
      "0.2 0.4132231404958678\n",
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      "0.4 0.4824561403508772\n",
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      "0.8 0.6190476190476191\n",
      "0.9 0.7\n",
      "0.95 0.7659574468085106\n",
      "CHEMBL1068886\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.39622641509433965\n",
      "0.2 0.46\n",
      "0.3 0.5058823529411764\n",
      "0.4 0.5384615384615384\n",
      "0.5 0.5714285714285714\n",
      "0.6 0.6067415730337079\n",
      "0.7 0.6612903225806451\n",
      "0.8 0.7164179104477612\n",
      "0.9 0.796875\n",
      "0.95 0.8311688311688312\n",
      "CHEMBL1068887\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.39622641509433965\n",
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      "0.6 0.6067415730337079\n",
      "0.7 0.6612903225806451\n",
      "0.8 0.7164179104477612\n",
      "0.9 0.796875\n",
      "0.95 0.8311688311688312\n",
      "CHEMBL2351085\n",
      "0.05 0.38571428571428573\n",
      "0.1 0.4166666666666667\n",
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      "0.3 0.504\n",
      "0.4 0.5634920634920635\n",
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      "0.7 0.6929824561403509\n",
      "0.8 0.7232142857142857\n",
      "0.9 0.7857142857142857\n",
      "0.95 0.8333333333333334\n",
      "CHEMBL4416273\n",
      "0.05 0.5210084033613446\n",
      "0.1 0.5555555555555556\n",
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      "0.8 0.7714285714285715\n",
      "0.9 0.81\n",
      "0.95 0.8383838383838383\n",
      "CHEMBL3073105\n",
      "0.05 0.31543624161073824\n",
      "0.1 0.3356164383561644\n",
      "0.2 0.36363636363636365\n",
      "0.3 0.3851851851851852\n",
      "0.4 0.40268456375838924\n",
      "0.5 0.42483660130718953\n",
      "0.6 0.4782608695652174\n",
      "0.7 0.5230769230769231\n",
      "0.8 0.584\n",
      "0.9 0.7478260869565218\n",
      "0.95 0.8181818181818182\n",
      "CHEMBL4262345\n",
      "0.05 0.3393939393939394\n",
      "0.1 0.42138364779874216\n",
      "0.2 0.4732824427480916\n",
      "0.3 0.5092592592592593\n",
      "0.4 0.5401459854014599\n",
      "0.5 0.5714285714285714\n",
      "0.6 0.5983606557377049\n",
      "0.7 0.6363636363636364\n",
      "0.8 0.6785714285714286\n",
      "0.9 0.7522123893805309\n",
      "0.95 0.8269230769230769\n",
      "CHEMBL4262391\n",
      "0.05 0.4409448818897638\n",
      "0.1 0.4666666666666667\n",
      "0.2 0.48717948717948717\n",
      "0.3 0.5227272727272727\n",
      "0.4 0.5818181818181818\n",
      "0.5 0.6181818181818182\n",
      "0.6 0.6422018348623854\n",
      "0.7 0.6666666666666666\n",
      "0.8 0.6915887850467289\n",
      "0.9 0.7373737373737373\n",
      "0.95 0.780952380952381\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedMorganFingerprint(x,3,1024,useFeatures=True),\"FeatMorgan3 (bits)\",accum)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# RDKit"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Branched (default)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=4),\"RDKit 4 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=5),\"RDKit 5 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=6),\"RDKit 6 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
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   ],
   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=7),\"RDKit 7 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## linear"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=4,branchedPaths=False),\"linear RDKit 4 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    }
   ],
   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=5,branchedPaths=False),\"linear RDKit 5 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHEMBL3706284\n",
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      "CHEMBL4262345\n",
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      "CHEMBL4262391\n",
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   ],
   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=6,branchedPaths=False),\"linear RDKit 6 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "CHEMBL1037716\n",
      "0.05 0.774468085106383\n",
      "0.1 0.7981927710843374\n",
      "0.2 0.8259629101283881\n",
      "0.3 0.8428781204111601\n",
      "0.4 0.8566978193146417\n",
      "0.5 0.8712241653418124\n",
      "0.6 0.8850746268656716\n",
      "0.7 0.8996960486322189\n",
      "0.8 0.9229534510433387\n",
      "0.9 0.9501607717041801\n",
      "0.95 0.960127591706539\n",
      "CHEMBL4392265\n",
      "0.05 0.5082644628099173\n",
      "0.1 0.5306451612903226\n",
      "0.2 0.5738539898132428\n",
      "0.3 0.6026490066225165\n",
      "0.4 0.6235864297253635\n",
      "0.5 0.6471518987341772\n",
      "0.6 0.6814310051107325\n",
      "0.7 0.7391304347826086\n",
      "0.8 0.7972972972972973\n",
      "0.9 0.8627450980392157\n",
      "0.95 0.8979591836734694\n",
      "CHEMBL1068886\n",
      "0.05 0.339662447257384\n",
      "0.1 0.3891213389121339\n",
      "0.2 0.4509433962264151\n",
      "0.3 0.5962732919254659\n",
      "0.4 0.6579925650557621\n",
      "0.5 0.708029197080292\n",
      "0.6 0.7364620938628159\n",
      "0.7 0.7686832740213523\n",
      "0.8 0.9083333333333333\n",
      "0.9 0.9884169884169884\n",
      "0.95 1.0\n",
      "CHEMBL1068887\n",
      "0.05 0.339662447257384\n",
      "0.1 0.3891213389121339\n",
      "0.2 0.4509433962264151\n",
      "0.3 0.5962732919254659\n",
      "0.4 0.6579925650557621\n",
      "0.5 0.708029197080292\n",
      "0.6 0.7364620938628159\n",
      "0.7 0.7686832740213523\n",
      "0.8 0.9083333333333333\n",
      "0.9 0.9884169884169884\n",
      "0.95 1.0\n",
      "CHEMBL2351085\n",
      "0.05 0.6493955094991365\n",
      "0.1 0.6985815602836879\n",
      "0.2 0.7452830188679245\n",
      "0.3 0.7720739219712526\n",
      "0.4 0.7945205479452054\n",
      "0.5 0.8187022900763359\n",
      "0.6 0.8475247524752475\n",
      "0.7 0.8734693877551021\n",
      "0.8 0.8991596638655462\n",
      "0.9 0.9344978165938864\n",
      "0.95 0.9557894736842105\n",
      "CHEMBL4416273\n",
      "0.05 0.5016181229773463\n",
      "0.1 0.5432525951557093\n",
      "0.2 0.583011583011583\n",
      "0.3 0.6398467432950191\n",
      "0.4 0.7\n",
      "0.5 0.7379912663755459\n",
      "0.6 0.7676348547717843\n",
      "0.7 0.7935779816513762\n",
      "0.8 0.8311111111111111\n",
      "0.9 0.8801652892561983\n",
      "0.95 0.9234042553191489\n",
      "CHEMBL3073105\n",
      "0.05 0.24523809523809523\n",
      "0.1 0.28465346534653463\n",
      "0.2 0.36211031175059955\n",
      "0.3 0.39487179487179486\n",
      "0.4 0.4293628808864266\n",
      "0.5 0.4642857142857143\n",
      "0.6 0.4935400516795866\n",
      "0.7 0.5384615384615384\n",
      "0.8 0.7580645161290323\n",
      "0.9 0.8366666666666667\n",
      "0.95 0.8914473684210527\n",
      "CHEMBL4262345\n",
      "0.05 0.5406871609403255\n",
      "0.1 0.5977653631284916\n",
      "0.2 0.665948275862069\n",
      "0.3 0.6938775510204082\n",
      "0.4 0.715311004784689\n",
      "0.5 0.7415966386554622\n",
      "0.6 0.7664543524416136\n",
      "0.7 0.7969094922737306\n",
      "0.8 0.8671497584541062\n",
      "0.9 0.9482352941176471\n",
      "0.95 0.973621103117506\n",
      "CHEMBL4262391\n",
      "0.05 0.4523809523809524\n",
      "0.1 0.48188405797101447\n",
      "0.2 0.5092592592592593\n",
      "0.3 0.533203125\n",
      "0.4 0.624765478424015\n",
      "0.5 0.6873706004140787\n",
      "0.6 0.7247311827956989\n",
      "0.7 0.7613882863340564\n",
      "0.8 0.7973273942093542\n",
      "0.9 0.8440366972477065\n",
      "0.95 0.8833333333333333\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:Chem.RDKFingerprint(x,maxPath=7,branchedPaths=False),\"linear RDKit 7 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Atom pairs and torsions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## count-based"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.05 0.32550335570469796\n",
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      "0.7 0.5514950166112956\n",
      "0.8 0.6209150326797386\n",
      "0.9 0.7134146341463414\n",
      "0.95 0.7697974217311234\n",
      "CHEMBL3705289\n",
      "0.05 0.24260355029585798\n",
      "0.1 0.27\n",
      "0.2 0.3021276595744681\n",
      "0.3 0.32971014492753625\n",
      "0.4 0.35564853556485354\n",
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      "0.95 0.6451612903225806\n",
      "CHEMBL3734283\n",
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      "CHEMBL3755859\n",
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      "0.8 0.5\n",
      "0.9 0.575\n",
      "0.95 0.6556962025316456\n",
      "CHEMBL3707859\n",
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      "CHEMBL3705383\n",
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      "CHEMBL3707832\n",
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      "CHEMBL839940\n",
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      "0.1 0.34\n",
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      "CHEMBL828963\n",
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      "CHEMBL827377\n",
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      "CHEMBL3707804\n",
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      "CHEMBL3796307\n",
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      "CHEMBL941996\n",
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      "CHEMBL1068888\n",
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      "CHEMBL1037716\n",
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      "CHEMBL4392265\n",
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      "CHEMBL1068886\n",
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      "CHEMBL1068887\n",
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      "0.6 0.5110132158590308\n",
      "0.7 0.5428571428571428\n",
      "0.8 0.5833333333333334\n",
      "0.9 0.6706349206349206\n",
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "0.7 0.6361256544502618\n",
      "0.8 0.6778350515463918\n",
      "0.9 0.7444168734491315\n",
      "0.95 0.8\n",
      "CHEMBL3073105\n",
      "0.05 0.2600896860986547\n",
      "0.1 0.28312412831241285\n",
      "0.2 0.31432973805855163\n",
      "0.3 0.34415584415584416\n",
      "0.4 0.38976377952755903\n",
      "0.5 0.4563106796116505\n",
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      "0.8 0.6207951070336392\n",
      "0.9 0.695024077046549\n",
      "0.95 0.7832817337461301\n",
      "CHEMBL4262345\n",
      "0.05 0.21124031007751937\n",
      "0.1 0.27221438645980256\n",
      "0.2 0.3284313725490196\n",
      "0.3 0.3684210526315789\n",
      "0.4 0.3948497854077253\n",
      "0.5 0.423828125\n",
      "0.6 0.4637002341920375\n",
      "0.7 0.5155555555555555\n",
      "0.8 0.5933734939759037\n",
      "0.9 0.6837416481069042\n",
      "0.95 0.7676470588235295\n",
      "CHEMBL4262391\n",
      "0.05 0.31906614785992216\n",
      "0.1 0.34306569343065696\n",
      "0.2 0.38461538461538464\n",
      "0.3 0.4439359267734554\n",
      "0.4 0.5186915887850467\n",
      "0.5 0.5617647058823529\n",
      "0.6 0.5981873111782477\n",
      "0.7 0.6287262872628726\n",
      "0.8 0.659016393442623\n",
      "0.9 0.7175324675324676\n",
      "0.95 0.7723076923076924\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetAtomPairFingerprint(x),\n",
    "                                 \"Atom Pairs (counts)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.4 0.4727272727272727\n",
      "0.5 0.5079365079365079\n",
      "0.6 0.5423728813559322\n",
      "0.7 0.5862068965517241\n",
      "0.8 0.6379310344827587\n",
      "0.9 0.7209302325581395\n",
      "0.95 0.8181818181818182\n",
      "CHEMBL650169\n",
      "0.05 0.37735849056603776\n",
      "0.1 0.4074074074074074\n",
      "0.2 0.46\n",
      "0.3 0.5\n",
      "0.4 0.5398230088495575\n",
      "0.5 0.5877192982456141\n",
      "0.6 0.6347826086956522\n",
      "0.7 0.6697247706422018\n",
      "0.8 0.7083333333333334\n",
      "0.9 0.7653061224489796\n",
      "0.95 0.8202247191011236\n",
      "CHEMBL909851\n",
      "0.05 0.072\n",
      "0.1 0.42857142857142855\n",
      "0.2 0.4852941176470588\n",
      "0.3 0.5180722891566265\n",
      "0.4 0.5522388059701493\n",
      "0.5 0.5897435897435898\n",
      "0.6 0.6285714285714286\n",
      "0.7 0.6623376623376623\n",
      "0.8 0.7205882352941176\n",
      "0.9 0.7741935483870968\n",
      "0.95 0.8095238095238095\n",
      "CHEMBL941996\n",
      "0.05 0.16666666666666666\n",
      "0.1 0.3793103448275862\n",
      "0.2 0.46987951807228917\n",
      "0.3 0.5064935064935064\n",
      "0.4 0.5584415584415584\n",
      "0.5 0.6043956043956044\n",
      "0.6 0.6538461538461539\n",
      "0.7 0.6962025316455697\n",
      "0.8 0.7613636363636364\n",
      "0.9 0.8309859154929577\n",
      "0.95 0.8676470588235294\n",
      "CHEMBL1068888\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.34782608695652173\n",
      "0.2 0.4788732394366197\n",
      "0.3 0.559322033898305\n",
      "0.4 0.5925925925925926\n",
      "0.5 0.625\n",
      "0.6 0.6842105263157895\n",
      "0.7 0.7241379310344828\n",
      "0.8 0.7777777777777778\n",
      "0.9 0.8695652173913043\n",
      "0.95 0.9130434782608695\n",
      "CHEMBL1037716\n",
      "0.05 0.5288461538461539\n",
      "0.1 0.5454545454545454\n",
      "0.2 0.5841584158415841\n",
      "0.3 0.6136363636363636\n",
      "0.4 0.6396396396396397\n",
      "0.5 0.6666666666666666\n",
      "0.6 0.6956521739130435\n",
      "0.7 0.7195121951219512\n",
      "0.8 0.7555555555555555\n",
      "0.9 0.8181818181818182\n",
      "0.95 0.8651685393258427\n",
      "CHEMBL4392265\n",
      "0.05 0.22727272727272727\n",
      "0.1 0.2625\n",
      "0.2 0.32051282051282054\n",
      "0.3 0.4\n",
      "0.4 0.43209876543209874\n",
      "0.5 0.48314606741573035\n",
      "0.6 0.5230769230769231\n",
      "0.7 0.5645161290322581\n",
      "0.8 0.6140350877192983\n",
      "0.9 0.7049180327868853\n",
      "0.95 0.8113207547169812\n",
      "CHEMBL1068886\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.34782608695652173\n",
      "0.2 0.4788732394366197\n",
      "0.3 0.559322033898305\n",
      "0.4 0.5925925925925926\n",
      "0.5 0.625\n",
      "0.6 0.6842105263157895\n",
      "0.7 0.7241379310344828\n",
      "0.8 0.7777777777777778\n",
      "0.9 0.8695652173913043\n",
      "0.95 0.9130434782608695\n",
      "CHEMBL1068887\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.34782608695652173\n",
      "0.2 0.4788732394366197\n",
      "0.3 0.559322033898305\n",
      "0.4 0.5925925925925926\n",
      "0.5 0.625\n",
      "0.6 0.6842105263157895\n",
      "0.7 0.7241379310344828\n",
      "0.8 0.7777777777777778\n",
      "0.9 0.8695652173913043\n",
      "0.95 0.9130434782608695\n",
      "CHEMBL2351085\n",
      "0.05 0.4166666666666667\n",
      "0.1 0.4473684210526316\n",
      "0.2 0.4925373134328358\n",
      "0.3 0.5230769230769231\n",
      "0.4 0.5538461538461539\n",
      "0.5 0.5901639344262295\n",
      "0.6 0.6229508196721312\n",
      "0.7 0.6666666666666666\n",
      "0.8 0.7017543859649122\n",
      "0.9 0.7636363636363637\n",
      "0.95 0.8148148148148148\n",
      "CHEMBL4416273\n",
      "0.05 0.3055555555555556\n",
      "0.1 0.3472222222222222\n",
      "0.2 0.410958904109589\n",
      "0.3 0.4666666666666667\n",
      "0.4 0.5151515151515151\n",
      "0.5 0.5522388059701493\n",
      "0.6 0.5967741935483871\n",
      "0.7 0.639344262295082\n",
      "0.8 0.6909090909090909\n",
      "0.9 0.7547169811320755\n",
      "0.95 0.8214285714285714\n",
      "CHEMBL3073105\n",
      "0.05 0.3116883116883117\n",
      "0.1 0.34210526315789475\n",
      "0.2 0.38202247191011235\n",
      "0.3 0.4189189189189189\n",
      "0.4 0.4533333333333333\n",
      "0.5 0.48484848484848486\n",
      "0.6 0.5217391304347826\n",
      "0.7 0.5737704918032787\n",
      "0.8 0.6268656716417911\n",
      "0.9 0.7288135593220338\n",
      "0.95 0.7894736842105263\n",
      "CHEMBL4262345\n",
      "0.05 0.30927835051546393\n",
      "0.1 0.3829787234042553\n",
      "0.2 0.4431818181818182\n",
      "0.3 0.4875\n",
      "0.4 0.5454545454545454\n",
      "0.5 0.5862068965517241\n",
      "0.6 0.6290322580645161\n",
      "0.7 0.6721311475409836\n",
      "0.8 0.7142857142857143\n",
      "0.9 0.7924528301886793\n",
      "0.95 0.8627450980392157\n",
      "CHEMBL4262391\n",
      "0.05 0.4230769230769231\n",
      "0.1 0.4583333333333333\n",
      "0.2 0.4852941176470588\n",
      "0.3 0.5076923076923077\n",
      "0.4 0.5492957746478874\n",
      "0.5 0.5915492957746479\n",
      "0.6 0.625\n",
      "0.7 0.6521739130434783\n",
      "0.8 0.6833333333333333\n",
      "0.9 0.7241379310344828\n",
      "0.95 0.7758620689655172\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetTopologicalTorsionFingerprint(x),\n",
    "                                 \"Topological Torsions (counts)\",accum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bit vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "0.1 0.45610278372591007\n",
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      "0.4 0.5446428571428571\n",
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      "0.6 0.5990990990990991\n",
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      "0.8 0.6955445544554455\n",
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      "CHEMBL3705289\n",
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      "CHEMBL1068888\n",
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      "CHEMBL4392265\n",
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      "CHEMBL1068886\n",
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      "CHEMBL1068887\n",
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      "0.8 0.6021505376344086\n",
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      "CHEMBL2351085\n",
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      "CHEMBL4416273\n",
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      "0.8 0.6872727272727273\n",
      "0.9 0.7626459143968871\n",
      "0.95 0.8195488721804511\n",
      "CHEMBL3073105\n",
      "0.05 0.32398753894080995\n",
      "0.1 0.3506172839506173\n",
      "0.2 0.3804878048780488\n",
      "0.3 0.4065934065934066\n",
      "0.4 0.4636118598382749\n",
      "0.5 0.5276073619631901\n",
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      "0.7 0.6068702290076335\n",
      "0.8 0.65625\n",
      "0.9 0.7222222222222222\n",
      "0.95 0.7946428571428571\n",
      "CHEMBL4262345\n",
      "0.05 0.3014018691588785\n",
      "0.1 0.3651315789473684\n",
      "0.2 0.4250559284116331\n",
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      "0.4 0.48286604361370716\n",
      "0.5 0.5105263157894737\n",
      "0.6 0.5451612903225806\n",
      "0.7 0.5888157894736842\n",
      "0.8 0.6450511945392492\n",
      "0.9 0.7173144876325088\n",
      "0.95 0.7908745247148289\n",
      "CHEMBL4262391\n",
      "0.05 0.38380281690140844\n",
      "0.1 0.403125\n",
      "0.2 0.4485981308411215\n",
      "0.3 0.49230769230769234\n",
      "0.4 0.5508196721311476\n",
      "0.5 0.588\n",
      "0.6 0.6194029850746269\n",
      "0.7 0.6468401486988847\n",
      "0.8 0.6785714285714286\n",
      "0.9 0.7313432835820896\n",
      "0.95 0.7729257641921398\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedAtomPairFingerprintAsBitVect(x),\n",
    "                                 \"Atom Pairs (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL3796307\n",
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      "0.1 0.39344262295081966\n",
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      "CHEMBL650169\n",
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      "0.95 0.8269230769230769\n",
      "CHEMBL909851\n",
      "0.05 0.10256410256410256\n",
      "0.1 0.453125\n",
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      "0.3 0.546875\n",
      "0.4 0.582089552238806\n",
      "0.5 0.6212121212121212\n",
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      "CHEMBL941996\n",
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      "0.6 0.6901408450704225\n",
      "0.7 0.7313432835820896\n",
      "0.8 0.7857142857142857\n",
      "0.9 0.847457627118644\n",
      "0.95 0.873015873015873\n",
      "CHEMBL1068888\n",
      "0.05 0.3137254901960784\n",
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      "0.8 0.7962962962962963\n",
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      "CHEMBL1037716\n",
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      "0.3 0.654320987654321\n",
      "0.4 0.6756756756756757\n",
      "0.5 0.7027027027027027\n",
      "0.6 0.7272727272727273\n",
      "0.7 0.75\n",
      "0.8 0.7866666666666666\n",
      "0.9 0.8484848484848485\n",
      "0.95 0.8888888888888888\n",
      "CHEMBL4392265\n",
      "0.05 0.25842696629213485\n",
      "0.1 0.29577464788732394\n",
      "0.2 0.3380281690140845\n",
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      "0.5 0.5\n",
      "0.6 0.5384615384615384\n",
      "0.7 0.576271186440678\n",
      "0.8 0.6307692307692307\n",
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      "0.95 0.803921568627451\n",
      "CHEMBL1068886\n",
      "0.05 0.3137254901960784\n",
      "0.1 0.35294117647058826\n",
      "0.2 0.4925373134328358\n",
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      "0.4 0.603448275862069\n",
      "0.5 0.6296296296296297\n",
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      "0.7 0.7407407407407407\n",
      "0.8 0.7962962962962963\n",
      "0.9 0.8666666666666667\n",
      "0.95 0.9111111111111111\n",
      "CHEMBL1068887\n",
      "0.05 0.3137254901960784\n",
      "0.1 0.35294117647058826\n",
      "0.2 0.4925373134328358\n",
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      "0.4 0.603448275862069\n",
      "0.5 0.6296296296296297\n",
      "0.6 0.6909090909090909\n",
      "0.7 0.7407407407407407\n",
      "0.8 0.7962962962962963\n",
      "0.9 0.8666666666666667\n",
      "0.95 0.9111111111111111\n",
      "CHEMBL2351085\n",
      "0.05 0.40298507462686567\n",
      "0.1 0.43283582089552236\n",
      "0.2 0.47540983606557374\n",
      "0.3 0.5081967213114754\n",
      "0.4 0.5423728813559322\n",
      "0.5 0.5789473684210527\n",
      "0.6 0.6153846153846154\n",
      "0.7 0.6545454545454545\n",
      "0.8 0.6923076923076923\n",
      "0.9 0.7551020408163265\n",
      "0.95 0.8222222222222222\n",
      "CHEMBL4416273\n",
      "0.05 0.3018867924528302\n",
      "0.1 0.34782608695652173\n",
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      "0.3 0.47058823529411764\n",
      "0.4 0.52\n",
      "0.5 0.5614035087719298\n",
      "0.6 0.6\n",
      "0.7 0.6470588235294118\n",
      "0.8 0.6956521739130435\n",
      "0.9 0.7608695652173914\n",
      "0.95 0.85\n",
      "CHEMBL3073105\n",
      "0.05 0.3181818181818182\n",
      "0.1 0.35\n",
      "0.2 0.390625\n",
      "0.3 0.43333333333333335\n",
      "0.4 0.47540983606557374\n",
      "0.5 0.515625\n",
      "0.6 0.55\n",
      "0.7 0.5892857142857143\n",
      "0.8 0.6428571428571429\n",
      "0.9 0.7368421052631579\n",
      "0.95 0.7884615384615384\n",
      "CHEMBL4262345\n",
      "0.05 0.410958904109589\n",
      "0.1 0.4647887323943662\n",
      "0.2 0.5151515151515151\n",
      "0.3 0.559322033898305\n",
      "0.4 0.5833333333333334\n",
      "0.5 0.6166666666666667\n",
      "0.6 0.6527777777777778\n",
      "0.7 0.6792452830188679\n",
      "0.8 0.7222222222222222\n",
      "0.9 0.803921568627451\n",
      "0.95 0.875\n",
      "CHEMBL4262391\n",
      "0.05 0.43478260869565216\n",
      "0.1 0.47619047619047616\n",
      "0.2 0.5084745762711864\n",
      "0.3 0.5344827586206896\n",
      "0.4 0.5689655172413793\n",
      "0.5 0.6\n",
      "0.6 0.631578947368421\n",
      "0.7 0.6545454545454545\n",
      "0.8 0.6792452830188679\n",
      "0.9 0.7254901960784313\n",
      "0.95 0.7659574468085106\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:rdMolDescriptors.GetHashedTopologicalTorsionFingerprintAsBitVect(x),\n",
    "                                 \"Topological Torsions (bits)\",accum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avalon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   ],
   "source": [
    "compareFPs(keep,lambda x:pyAvalonTools.GetAvalonFP(x,512),\"Avalon 512 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CHEMBL2187886\n",
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      "CHEMBL4334863\n",
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      "CHEMBL4334865\n"
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      "CHEMBL3073105\n",
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      "CHEMBL4262345\n",
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      "CHEMBL4262391\n",
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      "0.1 0.6889952153110048\n",
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      "0.7 0.8529411764705882\n",
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      "0.9 0.9457627118644067\n",
      "0.95 0.9620689655172414\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:pyAvalonTools.GetAvalonFP(x,1024),\"Avalon 1024 (bits)\",accum)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Avalon Counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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   "source": [
    "compareFPs(keep,lambda x:pyAvalonTools.GetAvalonCountFP(x,512),\"Avalon 512 (counts)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "0.95 0.8884156729131175\n",
      "CHEMBL4262391\n",
      "0.05 0.5389150943396226\n",
      "0.1 0.5695958948043618\n",
      "0.2 0.599250936329588\n",
      "0.3 0.6273864384463462\n",
      "0.4 0.646896551724138\n",
      "0.5 0.6610169491525424\n",
      "0.6 0.6783854166666666\n",
      "0.7 0.7921146953405018\n",
      "0.8 0.8808952837729817\n",
      "0.9 0.9184692179700499\n",
      "0.95 0.939932318104907\n"
     ]
    }
   ],
   "source": [
    "compareFPs(keep,lambda x:pyAvalonTools.GetAvalonCountFP(x,1024),\"Avalon 1024 (counts)\",accum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(accum,open('./results/fp_thresholds_scaffolds_accum.pkl','wb+'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "accum = pickle.load(open('./results/fp_thresholds_scaffolds_accum.pkl','rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand the plots, here are some median values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2 0.44\n",
      "0.6 0.58\n",
      "0.9 0.73\n"
     ]
    }
   ],
   "source": [
    "for thresh in (0.2, 0.6, 0.9):\n",
    "    print(f'{thresh} {np.median([x[\"Morgan2 (bits)\"][thresh] for x in accum.values()]):.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What these numbers mean: If we're using the bit-vector form of the Morgan2 fingerprint to do the similarity search, the median threshold across all the datasets to retrieve 80% of the actives (=missing 20%) was 0.44. Setting the threshold to 0.73, on the other hand, retrieved 10% of the actives in the median case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(15,6)\n",
    "fpnames = list(accum.values())[0].keys()\n",
    "d = []\n",
    "for nm in fpnames:\n",
    "    d.append([x[nm][0.9] for x in accum.values()])\n",
    "violinplot(d,showmedians=True);\n",
    "xticks(rotation=45,ha='right');\n",
    "xticks(range(1,len(d)+1),fpnames);\n",
    "title('0.9');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(15,6)\n",
    "fpnames = list(accum.values())[0].keys()\n",
    "d = []\n",
    "for nm in fpnames:\n",
    "    d.append([x[nm][0.6] for x in accum.values()])\n",
    "violinplot(d,showmedians=True);\n",
    "xticks(rotation=45,ha='right');\n",
    "xticks(range(1,len(d)+1),fpnames);\n",
    "title('0.6');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figsize(15,6)\n",
    "fpnames = list(accum.values())[0].keys()\n",
    "d = []\n",
    "for nm in fpnames:\n",
    "    d.append([x[nm][0.2] for x in accum.values()])\n",
    "violinplot(d,showmedians=True);\n",
    "xticks(rotation=45,ha='right');\n",
    "xticks(range(1,len(d)+1),fpnames);\n",
    "title('0.2');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.9.4"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}