UPGMA_classification-rdkit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RDKit version:  2021.03.3\n",
      "Pandas version: 1.2.4\n",
      "Numpy version: 1.20.3\n",
      "MatplotLib version: 3.4.2\n",
      "Sklearn version: 0.24.2\n",
      "Seaborn version: 0.11.1\n"
     ]
    }
   ],
   "source": [
    "import imp\n",
    "import pickle\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from ipywidgets import interact,fixed,IntSlider\n",
    "from IPython.display import SVG\n",
    "from matplotlib import rcParams\n",
    "from matplotlib.colors import hex2color\n",
    "from rdkit import Chem, rdBase\n",
    "from rdkit.Chem import rdFMCS, Draw, PandasTools, AllChem, DataStructs, Descriptors\n",
    "from rdkit.Chem import rdSubstructLibrary\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit import DataStructs\n",
    "\n",
    "print('RDKit version: ',rdBase.rdkitVersion)\n",
    "print('Pandas version:', pd.__version__)\n",
    "print('Numpy version:', np.__version__)\n",
    "print('MatplotLib version:', mpl.__version__)\n",
    "print('Sklearn version:', sklearn.__version__)\n",
    "print('Seaborn version:', sns.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'automated_series_classification.UPGMAclustering' from '/home/glandrum/miniconda3/envs/py38_automatedseries/lib/python3.8/site-packages/automated_series_classification/UPGMAclustering.py'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import automated_series_classification\n",
    "imp.reload(automated_series_classification)\n",
    "from automated_series_classification import utilsDataPrep\n",
    "from automated_series_classification import mainSeriesClassification\n",
    "imp.reload(mainSeriesClassification)\n",
    "from automated_series_classification import utilsDrawing\n",
    "from automated_series_classification import UPGMAclustering\n",
    "imp.reload(UPGMAclustering)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load ChEMBL database for substructure matching (constructed during data preprocessing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.9 s, sys: 620 ms, total: 21.6 s\n",
      "Wall time: 21.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "with open('./data/chembl27_sssdata.pkl','rb') as file:\n",
    "    chembldb = pickle.load(file)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The library has 1855322 molecules.\n",
      "CPU times: user 2.71 s, sys: 148 ms, total: 2.86 s\n",
      "Wall time: 2.86 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mols = rdSubstructLibrary.CachedTrustedSmilesMolHolder()\n",
    "fps = rdSubstructLibrary.PatternHolder()\n",
    "for smi,fp in chembldb:\n",
    "    mols.AddSmiles(smi)\n",
    "    fps.AddFingerprint(fp)\n",
    "chembl_library = rdSubstructLibrary.SubstructLibrary(mols,fps)\n",
    "chembldb = None\n",
    "print(f\"The library has {len(chembl_library)} molecules.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "Nchembl = len(chembl_library)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define Parameters, note that flimit corresponds to the specificity limit E(p) as described in the paper\n",
    "\n",
    "TIPS:dbpath is not automatically updated so if you would like to try to analyze different dataset, you should remove rdk_db or define dbpath by different name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "flimit=2e-3\n",
    "MinClusterSize=20 #20\n",
    "proj='CDK2Kinase'\n",
    "dbpath='./rdk_db/cdk2_db'\n",
    "filename='moldata_preprocessed.csv'\n",
    "datapath='./{0}/'.format(proj)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### UPGMA classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set \"calcDists\" to True only if the pairwise molecular distance matrix for clustering is not calculated yet (this will take a while). Set \"calcScores\" only if you are interested in the intra-cluster distance metric, this slows down the clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "read 3187 molecules\n",
      "creating database\n"
     ]
    }
   ],
   "source": [
    "UPGMAClassification=mainSeriesClassification.Classification(proj, datapath, dbpath, filename, chembl_library, \n",
    "                                                            flimit, MinClusterSize, clustering='UPGMA', \n",
    "                                                            calcDists=True, calcScores=False, useArthor=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time elapsed during the calculation: 1121.0\n"
     ]
    }
   ],
   "source": [
    "#imp.reload(UPGMAclustering)\n",
    "#imp.reload(mainSeriesClassification)\n",
    "start=time.time()\n",
    "UPGMAClassification.ApplyClustering()\n",
    "end=time.time()\n",
    "print(f\"Time elapsed during the calculation: {end-start:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Benchmark against human-defined classification (seriescolumn is the column with the human series assignment to each molecule in the dataframe moldata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "UPGMAClassification.CalculatePerformance(seriescolumn='series assignment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "moldata_proj=UPGMAClassification.moldata_proj\n",
    "ProjectClusters=UPGMAClassification.MCSdict\n",
    "PerformanceClusters=UPGMAClassification.PerformanceClusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([6347, 6149, 6317, 6319, 6016, 6181, 6230, 6147, 5970, 6313, 6316, 6168, 6277, 6104, 6020, 6198, 6105, 6151, 6067, 5996, 5740])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "UPGMAClassification.MCSdict.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate performance metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "UPGMAClassification.CalculatePerformance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fraction of assigned molecules: 0.8650768748038908 , ambiguity score: 1.1454479506710191\n"
     ]
    }
   ],
   "source": [
    "Nmol=len(moldata_proj)\n",
    "Nrep=sum(moldata_proj['ClusterID'].map(lambda x: len(x)).tolist())\n",
    "Nunassigned=len(moldata_proj.loc[moldata_proj['ClusterID'].map(lambda x: len(x)==0)])\n",
    "frac_assigned=(Nmol-Nunassigned)/Nmol\n",
    "a=Nrep/(Nmol-Nunassigned)\n",
    "print('fraction of assigned molecules:',frac_assigned,', ambiguity score:',a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot benchmarking between automatically identified and human defined series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "rearrange performance results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaflist=list(set(moldata_proj['scaffold'].tolist()))\n",
    "scaflist.sort()\n",
    "N_auto_series=len(PerformanceClusters['recall'])\n",
    "LinkVector=PerformanceClusters['linked series']\n",
    "\n",
    "dict_recall={LinkVector[ind,0]:np.zeros(len(scaflist)) for ind in range(N_auto_series)}\n",
    "dict_prec={LinkVector[ind,0]:np.zeros(len(scaflist)) for ind in range(N_auto_series)}\n",
    "for ind in range(N_auto_series):\n",
    "    scafind=np.where(np.array(scaflist)==LinkVector[ind,1])\n",
    "    dict_recall[LinkVector[ind,0]][scafind]=PerformanceClusters['recall'][ind]\n",
    "    dict_prec[LinkVector[ind,0]][scafind]=PerformanceClusters['precision'][ind]\n",
    "    \n",
    "keylist=LinkVector[:,0].tolist()\n",
    "keylist.sort()\n",
    "dict_recall_sorted={k:dict_recall[k] for k in keylist}\n",
    "dict_prec_sorted={k:dict_prec[k] for k in keylist}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "legend=['Series '+s[-2:] for s in scaflist]\n",
    "rcParams.update({'figure.autolayout': True})\n",
    "fig,ax=plt.subplots(2,1,figsize=(10,10))\n",
    "fig,_=utilsDrawing.barplot_vertical(fig, ax[0],dict_recall_sorted,'recall linked h. series',[],['']*len(keylist),1.02)\n",
    "fig,_=utilsDrawing.barplot_vertical(fig, ax[1],dict_prec_sorted,'precision linked h. series',legend,keylist,1.02)\n",
    "ax[1].set_xlabel('ID automatically-identified series',fontsize=22)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draw MCS of automatically-identified series and linked human-defined series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "imp.reload(utilsDrawing)\n",
    "def renderMCS(seriesid,newscafslist,newscafsnames,linkedscafslist,linkedscafsnames):\n",
    "    listid=np.where(np.array(newscafsnames)==seriesid)[0][0]\n",
    "    svg_new=utilsDrawing.moltosvg(Chem.MolFromSmarts(newscafslist[listid]), molSize=(450,250))\n",
    "    svg_linked=utilsDrawing.moltosvg(Chem.MolFromSmarts(linkedscafslist[listid]), molSize=(450,250))\n",
    "    labels=[newscafsnames[listid],linkedscafsnames[listid]]\n",
    "    svgGrid = utilsDrawing.SvgsToGrid([svg_new,svg_linked], labels=labels, svgsPerRow=2, molSize=(450,250))\n",
    "    return(SVG(svgGrid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'./CDK2Kinase/'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datapath"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dd26780701c94c718c9fc3706742bb73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='seriesid', options=('5740', '5970', '5996', '6016', '6020', '6067'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "seriesdata_proj=pd.read_csv('{0}seriesdata.csv'.format(datapath))\n",
    "LinkVector_sorted=np.array([[x,y] for x,y in sorted(zip(LinkVector[:,0],LinkVector[:,1]))])\n",
    "linkedScafsList=[seriesdata_proj['MCSsampled'].loc[seriesdata_proj['ScafName']==x].iloc[0] for x in LinkVector_sorted[:,1]]\n",
    "linkedScafsNames=[x for x in LinkVector_sorted[:,1]]\n",
    "MCSlist=[ProjectClusters[int(k)][2] for k in keylist]\n",
    "\n",
    "interact(renderMCS, seriesid=keylist, newscafslist=fixed(MCSlist),newscafsnames=fixed(keylist),linkedscafslist=fixed(linkedScafsList),linkedscafsnames=fixed(linkedScafsNames));\n"
   ]
  }
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