From b7b5fdf3d245ad6329144a4b2bb519ce08c55311 Mon Sep 17 00:00:00 2001 From: Paula Kramer Date: Mon, 24 Apr 2023 14:19:37 +0200 Subject: [PATCH] solutions for ligand-based screening --- Pipelines/data/filtered_compounds.csv | 12 + Pipelines/ligand_screening_sol.ipynb | 1364 +++++++++++++++++++++++++ 2 files changed, 1376 insertions(+) create mode 100644 Pipelines/data/filtered_compounds.csv create mode 100644 Pipelines/ligand_screening_sol.ipynb diff --git a/Pipelines/data/filtered_compounds.csv b/Pipelines/data/filtered_compounds.csv new file mode 100644 index 0000000..e551683 --- /dev/null +++ b/Pipelines/data/filtered_compounds.csv @@ -0,0 +1,12 @@ +chembl_id,canonical_smiles +CHEMBL2447950,Cl.CCOc1cc2ncnc(Nc3cccc(Cl)c3)c2cc1OCC +CHEMBL3671504,Fc1cc(Br)ccc1Nc2ncnc3cc(OCCC=C)c(NC(=O)C=C)cc23 +CHEMBL138940,Fc1ccc(Nc2ncnc3cnc(NC(=O)\C=C\C(=O)NCCCN4CCOCC4)cc23)cc1Cl +CHEMBL513208,CN(C)Cc1ccc(Nc2nc3ncnc(Nc4ccc(F)c(Cl)c4)c3s2)cc1 +CHEMBL3952157,COc1ccc(cc1)c2oc3ncnc(N[C@H](CO)c4ccccc4)c3c2 +CHEMBL311109,NC(=O)C1CCN(Cc2ccc(cc2)c3cc4ncnc(Nc5ccc6[nH]ccc6c5)c4s3)CC1 +CHEMBL14874,CN(CCCOc1ccc2ncnc(Nc3ccc(OCc4ccccc4)cc3)c2c1)CCS(=O)(=O)C +CHEMBL3805299,COCCOc1cc2ncnc(Nc3ccc(C)c(c3)[N+](=O)[O-])c2c4OCCOc14 +CHEMBL206029,CS(=O)(=O)CCNC(=O)NCc1cccc(n1)C#Cc2cncnc2Nc3ccc(OCc4cccc(F)c4)c(Cl)c3 +CHEMBL490267,CC(C)(C)c1ccc(OC[C@H](Cn2ccnc2[N+](=O)[O-])OC(=O)c3ccc(NC=C4C(=O)C=CC4=O)cc3)cc1 +CHEMBL2315698,CC(C)[C@@H](NC1=CC(=O)C(=C(C\C=C(/C)\CC[C@@]2(C)CCC[C@@H](C)C2=C)C1=O)O)C(=O)O diff --git a/Pipelines/ligand_screening_sol.ipynb b/Pipelines/ligand_screening_sol.ipynb new file mode 100644 index 0000000..664ff08 --- /dev/null +++ b/Pipelines/ligand_screening_sol.ipynb @@ -0,0 +1,1364 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Ligand-based Screening - Solution" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Your task here is to implement a virtual screening pipeline for unknown compounds using a machine learning approach. " + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [], + "source": [ + "# Do not forget to import the needed libraries\n", + "import pandas as pd\n", + "from rdkit import Chem\n", + "from rdkit.Chem import AllChem, Descriptors, Draw, DataStructs\n", + "from rdkit.Chem.AllChem import GetMorganFingerprintAsBitVect\n", + "from rdkit.ML.Cluster import Butina\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn import metrics\n", + "from sklearn.metrics import recall_score\n", + "\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 1: Data Preparation\n", + "To make the ML part easier for you, we have prepared an EGFR dataset for training (`./data/ChEMBL25_activities_EGFR.csv`). Your task is to predict whether a compound is active or not. For the model, you will encode your compounds using fingerprints. Like before, first read in the data into a data frame, then calculate the Morgan fingerprints for all given compounds. " + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0chembl_idIC50unitscanonical_smilespIC50
01777CHEMBL20786977.0nMFc1cccc(COc2ccc(Nc3ncncc3C#Cc4ncccn4)cc2Cl)c17.113509
15785CHEMBL3940060330.0nMFc1ccc(Nc2ncnc3cc4OC(=O)N(CCOC(=O)CCl)c4cc23)c...6.481486
26373CHEMBL36789511.0nMCC(C)n1c(Nc2cccc(c2)C(F)(F)F)nc3cnc(Nc4ccc(cc4...9.000000
32442CHEMBL50403440.0nMCS(=O)(=O)CCNC(=O)O[C@H]1CN[C@@H](C1)C#Cc2cc3n...7.397940
41084CHEMBL15879743000.0nMCn1c(SSc2c(C(=O)NCC(O)CO)c3ccccc3n2C)c(C(=O)NC...4.366532
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" + ], + "text/plain": [ + " Unnamed: 0 chembl_id IC50 units \n", + "0 1777 CHEMBL207869 77.0 nM \\\n", + "1 5785 CHEMBL3940060 330.0 nM \n", + "2 6373 CHEMBL3678951 1.0 nM \n", + "3 2442 CHEMBL504034 40.0 nM \n", + "4 1084 CHEMBL158797 43000.0 nM \n", + "\n", + " canonical_smiles pIC50 \n", + "0 Fc1cccc(COc2ccc(Nc3ncncc3C#Cc4ncccn4)cc2Cl)c1 7.113509 \n", + "1 Fc1ccc(Nc2ncnc3cc4OC(=O)N(CCOC(=O)CCl)c4cc23)c... 6.481486 \n", + "2 CC(C)n1c(Nc2cccc(c2)C(F)(F)F)nc3cnc(Nc4ccc(cc4... 9.000000 \n", + "3 CS(=O)(=O)CCNC(=O)O[C@H]1CN[C@@H](C1)C#Cc2cc3n... 7.397940 \n", + "4 Cn1c(SSc2c(C(=O)NCC(O)CO)c3ccccc3n2C)c(C(=O)NC... 4.366532 " + ] + }, + "execution_count": 328, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in file \n", + "df=pd.read_csv('./data/ChEMBL25_activities_EGFR.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [], + "source": [ + "# morgan fingerprint \n", + "mols = []\n", + "for entry in df['canonical_smiles']:\n", + " mols.append(Chem.MolFromSmiles(entry))\n", + "\n", + "fps = []\n", + "for mol in mols:\n", + " fps.append(GetMorganFingerprintAsBitVect(mol,2))\n", + "\n", + "df['molecule'] = mols\n", + "df['morgan'] = fps\n", + "\n", + "\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, add a column in your data frame indicating whether a compound is active or not. Set the values 1, 0 for active, inactive compounds respectively using activity cutoff to pIC50 = 6.3." + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chembl_idcanonical_smilespIC50moleculemorganactive
0CHEMBL207869Fc1cccc(COc2ccc(Nc3ncncc3C#Cc4ncccn4)cc2Cl)c17.113509<rdkit.Chem.rdchem.Mol object at 0x18e072b00>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...1
1CHEMBL3940060Fc1ccc(Nc2ncnc3cc4OC(=O)N(CCOC(=O)CCl)c4cc23)c...6.481486<rdkit.Chem.rdchem.Mol object at 0x18db68e20>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...1
2CHEMBL3678951CC(C)n1c(Nc2cccc(c2)C(F)(F)F)nc3cnc(Nc4ccc(cc4...9.000000<rdkit.Chem.rdchem.Mol object at 0x18db692a0>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...1
3CHEMBL504034CS(=O)(=O)CCNC(=O)O[C@H]1CN[C@@H](C1)C#Cc2cc3n...7.397940<rdkit.Chem.rdchem.Mol object at 0x18db6b9a0>[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...1
4CHEMBL158797Cn1c(SSc2c(C(=O)NCC(O)CO)c3ccccc3n2C)c(C(=O)NC...4.366532<rdkit.Chem.rdchem.Mol object at 0x18d1694e0>[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...0
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" + ], + "text/plain": [ + " chembl_id canonical_smiles pIC50 \n", + "0 CHEMBL207869 Fc1cccc(COc2ccc(Nc3ncncc3C#Cc4ncccn4)cc2Cl)c1 7.113509 \\\n", + "1 CHEMBL3940060 Fc1ccc(Nc2ncnc3cc4OC(=O)N(CCOC(=O)CCl)c4cc23)c... 6.481486 \n", + "2 CHEMBL3678951 CC(C)n1c(Nc2cccc(c2)C(F)(F)F)nc3cnc(Nc4ccc(cc4... 9.000000 \n", + "3 CHEMBL504034 CS(=O)(=O)CCNC(=O)O[C@H]1CN[C@@H](C1)C#Cc2cc3n... 7.397940 \n", + "4 CHEMBL158797 Cn1c(SSc2c(C(=O)NCC(O)CO)c3ccccc3n2C)c(C(=O)NC... 4.366532 \n", + "\n", + " molecule \n", + "0 \\\n", + "1 \n", + "2 \n", + "3 \n", + "4 \n", + "\n", + " morgan active \n", + "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 1 \n", + "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... 1 \n", + "2 [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ... 1 \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 1 \n", + "4 [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... 0 " + ] + }, + "execution_count": 330, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# compound activity\n", + "df['active'] = df['pIC50'].map(lambda x:x>6.3).astype(int)\n", + "\n", + "# remove unnecessary columns from dataframe \n", + "df.drop(['IC50','units','Unnamed: 0'], axis=1, inplace=True)\n", + "df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 2: Machine Learning \n", + "\n", + "Train a Random Forest model using the data prepared above. Set the random state to 0 for reproducible results. Split your data into 80% training and 20% test data and set the seed to 1234 for reproducible results. " + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [], + "source": [ + "# random forest model \n", + "\n", + "def build_random_forest_model(X_train, X_test, y_train, y_test, nb_tree=100, nb_dec=3):\n", + " \"\"\"\n", + " Trains a Random Forest model with ``nb_tree`` trees using X_train and y_train and predict using X_test. \n", + " \n", + " Parameters\n", + " ----------\n", + " X_train: array_like\n", + " Input data to train the model\n", + " X_test: array_like\n", + " Input data to test the model \n", + " y_train: array\n", + " Output value in the train set\n", + " y_test: array\n", + " Output value in the test set\n", + " nb_tree: int\n", + " number of trees to build for the forest\n", + " nb_dec: int\n", + " number of decimals to considers for the metrics\n", + " \n", + " Returns\n", + " -------\n", + " dict:\n", + " ``\"Probability\"``\n", + " probability of belonging to class 1 and class 0 in (`array`) type.\n", + " ``'Accuracy'``\n", + " accuracy of the model (`float`).\n", + " ``'AUC'`` AUC of the model (`float`).\n", + " ``'Sensitivity'`` Sensitivity of the model (`float`).\n", + " ``'Specificity'`` Specificity of the model (`float`).\n", + " \"\"\"\n", + " \n", + " # Initialize Random Forest with nb_tree of trees\n", + " forest = RandomForestClassifier(n_estimators=nb_tree)\n", + " \n", + " # Train the model\n", + " forest.fit(X_train, y_train)\n", + " \n", + " # Obtain probability of belonging to class 0 and 1\n", + " y_prob = forest.predict_proba(X_test)\n", + " \n", + " # Predict the output class of the test set\n", + " y_pred = forest.predict(X_test)\n", + " \n", + " # Compute metrics\n", + " accuracy = metrics.accuracy_score(y_test, y_pred).round(nb_dec)\n", + " auc = metrics.roc_auc_score(y_test, y_prob[:,1]).round(nb_dec)\n", + " sensitivity = metrics.recall_score(y_test, y_pred, pos_label=1).round(nb_dec)\n", + " specificity = metrics.recall_score(y_test, y_pred, pos_label=0).round(nb_dec)\n", + " \n", + " return {'Probability': y_prob,\n", + " 'Accuracy': accuracy,\n", + " 'AUC': auc,\n", + " 'Sensitivity': sensitivity,\n", + " 'Specificity': specificity, \n", + " 'forest': forest}" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [], + "source": [ + "# Split data into training set (80%) and test set (20%), shuffling the data and setting the seed\n", + "output = df['active'].tolist()\n", + "x_morgan_train, x_morgan_test, y_morgan_train, y_morgan_test = train_test_split(fps, output,\n", + " test_size=0.2,\n", + " shuffle=True,\n", + " random_state=42)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To evaluate your model, calculate the accuracy, AUC, sensitivity, and specificity values (for sensitivity and specificity search for the sklearn function recall_score)." + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Probability': array([[0.95, 0.05],\n", + " [0.88, 0.12],\n", + " [0.14, 0.86],\n", + " ...,\n", + " [1. , 0. ],\n", + " [0.96, 0.04],\n", + " [0.07, 0.93]]),\n", + " 'Accuracy': 0.84,\n", + " 'AUC': 0.911,\n", + " 'Sensitivity': 0.903,\n", + " 'Specificity': 0.75,\n", + " 'forest': RandomForestClassifier()}" + ] + }, + "execution_count": 333, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# model evaluation\n", + "result_morgan = build_random_forest_model(x_morgan_train, x_morgan_test, y_morgan_train, y_morgan_test)\n", + "forest = result_morgan['forest']\n", + "result_morgan" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Task 3: Compound Filtering \n", + "\n", + "Filter the compounds in \"data/test_compounds_EGFR.csv\" down to 20 compounds. For this, you can combine I) classical similarity searches/substructure searches II) predictions via a machine learning approach, III) filtering criteria based on molecular properties (Lipinski's rule of five) IV) and/or clustering. Note that you will predict the binding affinity and binding mode of your selected subset via docking for further filtering. It might be wise to have a selected set of diverse compounds, e.g. obtained using different selection criteria.\n", + "\n", + "\n", + "* Read in the data containing the unknown compounds. Start your filtering with an activity prediction using the trained random forest model. If you have time, feel free to try out different models (e.g. what you have seen in previous sessions) and choose your favorite one for the filtering. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0chembl_idcanonical_smiles
01927CHEMBL101253Clc1ccc(Nc2nnc(Cc3ccncc3)c4ccccc24)cc1
1212CHEMBL107472CNc1ncc2C=C(C(=O)N(C)c2n1)c3c(Cl)cccc3Cl
23089CHEMBL1077068CC1=C(C(NC(=N1)Nc2ccc(cc2)S(=O)(=O)Nc3ncccn3)c...
33126CHEMBL1088240COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2...
43125CHEMBL1092382CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1
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" + ], + "text/plain": [ + " Unnamed: 0 chembl_id \n", + "0 1927 CHEMBL101253 \\\n", + "1 212 CHEMBL107472 \n", + "2 3089 CHEMBL1077068 \n", + "3 3126 CHEMBL1088240 \n", + "4 3125 CHEMBL1092382 \n", + "\n", + " canonical_smiles \n", + "0 Clc1ccc(Nc2nnc(Cc3ccncc3)c4ccccc24)cc1 \n", + "1 CNc1ncc2C=C(C(=O)N(C)c2n1)c3c(Cl)cccc3Cl \n", + "2 CC1=C(C(NC(=N1)Nc2ccc(cc2)S(=O)(=O)Nc3ncccn3)c... \n", + "3 COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2... \n", + "4 CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1 " + ] + }, + "execution_count": 334, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# read in unknown compounds \n", + "data_test = pd.read_csv(\"./data/test_compounds_EGFR.csv\")\n", + "data_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [], + "source": [ + "# Convert smiles in data_test to morgan fingerprints\n", + "smiles_test = data_test[\"canonical_smiles\"].tolist()\n", + "morgan_fp_test = []\n", + "test_mols = []\n", + "\n", + "for smiles in smiles_test:\n", + " mol = Chem.MolFromSmiles(smiles)\n", + " test_mols.append(mol)\n", + " morgan_fp_test.append(GetMorganFingerprintAsBitVect(mol,2))\n", + "\n", + "data_test[\"morgan\"] = morgan_fp_test\n", + "data_test[\"molecule\"] = test_mols\n", + "data_test.drop(['Unnamed: 0'], axis=1, inplace=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 336, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chembl_idcanonical_smilesmorganmolecule
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2CHEMBL1077068CC1=C(C(NC(=N1)Nc2ccc(cc2)S(=O)(=O)Nc3ncccn3)c...[0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18da63ee0>
3CHEMBL1088240COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...<rdkit.Chem.rdchem.Mol object at 0x18d752020>
4CHEMBL1092382CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d752260>
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" + ], + "text/plain": [ + " chembl_id canonical_smiles \n", + "0 CHEMBL101253 Clc1ccc(Nc2nnc(Cc3ccncc3)c4ccccc24)cc1 \\\n", + "1 CHEMBL107472 CNc1ncc2C=C(C(=O)N(C)c2n1)c3c(Cl)cccc3Cl \n", + "2 CHEMBL1077068 CC1=C(C(NC(=N1)Nc2ccc(cc2)S(=O)(=O)Nc3ncccn3)c... \n", + "3 CHEMBL1088240 COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2... \n", + "4 CHEMBL1092382 CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1 \n", + "\n", + " morgan \n", + "0 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \\\n", + "1 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "2 [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ... \n", + "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "\n", + " molecule \n", + "0 \n", + "1 \n", + "2 \n", + "3 \n", + "4 " + ] + }, + "execution_count": 336, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_test.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 338, + "metadata": {}, + "outputs": [], + "source": [ + "# Predict the output class of the test set: active/inactive and add to dataframe \n", + "y_pred = forest.predict(morgan_fp_test)\n", + "data_test['active/inactive'] = y_pred\n", + "\n", + "# Obtain probability of belonging to a class active/inactive for the test set and add to dataframe\n", + "y_prob = forest.predict_proba(morgan_fp_test)\n", + "data_test['0 prob'] = y_prob[:,0]\n", + "data_test['1 prob'] = y_prob[:,1]" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of compounds predicted active: 659.\n" + ] + } + ], + "source": [ + "nb_active = (data_test['active/inactive'] == 1).sum()\n", + "print(f'Number of compounds predicted active: {nb_active}.')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chembl_idcanonical_smilesmorganmoleculeactive/inactive0 prob1 prob
3CHEMBL1088240COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ...<rdkit.Chem.rdchem.Mol object at 0x18d752020>10.2900.710
4CHEMBL1092382CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d752260>10.2850.715
12CHEMBL1097368C(Cc1cc2c(Nc3ccc4[nH]ccc4c3)ncnc2[nH]1)c5ccccc5[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d7529e0>10.3200.680
14CHEMBL113023COc1ccc(Nc2ncc3C=C(C(=O)N(C)c3n2)c4c(Cl)cccc4C...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d752b60>10.3300.670
15CHEMBL113070CN1C(=O)C(=Cc2cnc(Nc3ccc(CCCC(=O)O)cc3)nc12)c4...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d752c20>10.2400.760
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" + ], + "text/plain": [ + " chembl_id canonical_smiles \n", + "3 CHEMBL1088240 COc1cc(Nc2c(cnc3cc(\\C=C\\CCN4CCN(C)CC4)c(OC)cc2... \\\n", + "4 CHEMBL1092382 CCC(=O)Nc1ccc2nccc(Nc3cccc(Br)c3)c2c1 \n", + "12 CHEMBL1097368 C(Cc1cc2c(Nc3ccc4[nH]ccc4c3)ncnc2[nH]1)c5ccccc5 \n", + "14 CHEMBL113023 COc1ccc(Nc2ncc3C=C(C(=O)N(C)c3n2)c4c(Cl)cccc4C... \n", + "15 CHEMBL113070 CN1C(=O)C(=Cc2cnc(Nc3ccc(CCCC(=O)O)cc3)nc12)c4... \n", + "\n", + " morgan \n", + "3 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, ... \\\n", + "4 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "12 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "14 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "15 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "\n", + " molecule active/inactive 0 prob \n", + "3 1 0.290 \\\n", + "4 1 0.285 \n", + "12 1 0.320 \n", + "14 1 0.330 \n", + "15 1 0.240 \n", + "\n", + " 1 prob \n", + "3 0.710 \n", + "4 0.715 \n", + "12 0.680 \n", + "14 0.670 \n", + "15 0.760 " + ] + }, + "execution_count": 340, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create new dataframe containing only compounds predicted to be active \n", + "active_df = data_test[data_test['active/inactive'] == 1]\n", + "active_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a first filtering we have chosen to apply Lipinski's rule of five. We filter out all compounds violating more than one of the Lipinski rules. " + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "def test_rule_of_five(smi):\n", + " \"\"\"\n", + " Checks if molecule fulfills the Lipinski's rule of five. Calculates the molecular descriptors and checks for all rules. \n", + " We allow one mismatch in the rules. \n", + " \n", + " Parameters\n", + " ----------\n", + " smi: string\n", + " smiles string of compound \n", + " \n", + " Returns\n", + " -------\n", + " bool: indicating True if Lipinski's rule of five holds. \n", + " \"\"\"\n", + "\n", + " m = Chem.MolFromSmiles(smi)\n", + " \n", + " # Calculate rule of five chemical properties\n", + " MW = Descriptors.ExactMolWt(m)\n", + " HBA = Descriptors.NumHAcceptors(m)\n", + " HBD = Descriptors.NumHDonors(m)\n", + " LogP = Descriptors.MolLogP(m)\n", + " \n", + " # Rule of five conditions\n", + " conditions = [MW <= 500, HBA <= 10, HBD <= 5, LogP <= 5]\n", + " # Return True if no more than one out of four conditions is violated\n", + " return conditions.count(True) >= 3\n" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(530, 8)" + ] + }, + "execution_count": 342, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filter lipinski, save filtered compounds to dataframe \n", + "filtered_df = active_df.copy(deep=True)\n", + "filtered_df['lipinski'] = active_df['canonical_smiles'].apply(test_rule_of_five).copy(deep=True)\n", + "filtered_df = filtered_df[filtered_df['lipinski']]\n", + "filtered_df.shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a next filtering step we apply Butina clustering. First we calculate the pairwise distances, then we run the clustering algorithm. Afterwards we select a compound from each cluster. This reduces the number of compounds and makes the filtered set more diverse. " + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": {}, + "outputs": [], + "source": [ + "def tanimoto_distance_matrix(fp_list):\n", + " \"\"\"Calculate distance matrix for fingerprint list\"\"\"\n", + " dissimilarity_matrix = []\n", + " # Notice how we are deliberately skipping the first and last items in the list\n", + " # because we don't need to compare them against themselves\n", + " for i in range(1, len(fp_list)):\n", + " # Compare the current fingerprint against all the previous ones in the list\n", + " similarities = DataStructs.BulkTanimotoSimilarity(fp_list[i], fp_list[:i])\n", + " # Since we need a distance matrix, calculate 1-x for every element in similarity matrix\n", + " dissimilarity_matrix.extend([1 - x for x in similarities])\n", + " return dissimilarity_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [], + "source": [ + "# Fingerprints as lists\n", + "fingerprints=filtered_df[\"morgan\"].to_list()\n", + "# Calculate distance matrix\n", + "dist_matrix = tanimoto_distance_matrix(fingerprints)\n", + "# Define distance cut-off\n", + "cutoff=0.7" + ] + }, + { + "cell_type": "code", + "execution_count": 345, + "metadata": {}, + "outputs": [], + "source": [ + "# clustering \n", + "clusters = Butina.ClusterData(dist_matrix, len(fingerprints), cutoff, isDistData=True)\n", + "clusters = sorted(clusters, key=len, reverse=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": {}, + "outputs": [], + "source": [ + "# get indexes of compounds we want to keep (one index for each cluster)\n", + "cluster_indexes = []\n", + "for cluster in clusters: \n", + " cluster_indexes.append(cluster[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(40, 8)" + ] + }, + "execution_count": 347, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# create dataframe with remaining compounds \n", + "cluster_df = filtered_df.iloc[cluster_indexes]\n", + "cluster_df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chembl_idcanonical_smilesmorganmoleculeactive/inactive0 prob1 problipinski
347CHEMBL2447950Cl.CCOc1cc2ncnc(Nc3cccc(Cl)c3)c2cc1OCC[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d7622c0>10.040.96True
615CHEMBL3671504Fc1cc(Br)ccc1Nc2ncnc3cc(OCCC=C)c(NC(=O)C=C)cc23[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d76ec80>10.001.00True
106CHEMBL138940Fc1ccc(Nc2ncnc3cnc(NC(=O)\\C=C\\C(=O)NCCCN4CCOCC...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d756f20>10.010.99True
757CHEMBL394056COCc1ccn2ncnc(Nc3ccc4c(cnn4Cc5cccc(F)c5)c3)c12[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d775780>10.090.91True
509CHEMBL3301625COCCOc1ccc(Nc2ncc(F)c(Nc3cccc(NC(=O)C=C)c3)n2)cc1[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d769cc0>10.300.70True
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" + ], + "text/plain": [ + " chembl_id canonical_smiles \n", + "347 CHEMBL2447950 Cl.CCOc1cc2ncnc(Nc3cccc(Cl)c3)c2cc1OCC \\\n", + "615 CHEMBL3671504 Fc1cc(Br)ccc1Nc2ncnc3cc(OCCC=C)c(NC(=O)C=C)cc23 \n", + "106 CHEMBL138940 Fc1ccc(Nc2ncnc3cnc(NC(=O)\\C=C\\C(=O)NCCCN4CCOCC... \n", + "757 CHEMBL394056 COCc1ccn2ncnc(Nc3ccc4c(cnn4Cc5cccc(F)c5)c3)c12 \n", + "509 CHEMBL3301625 COCCOc1ccc(Nc2ncc(F)c(Nc3cccc(NC(=O)C=C)c3)n2)cc1 \n", + "\n", + " morgan \n", + "347 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \\\n", + "615 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... \n", + "106 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "757 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "509 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... \n", + "\n", + " molecule active/inactive 0 prob \n", + "347 1 0.04 \\\n", + "615 1 0.00 \n", + "106 1 0.01 \n", + "757 1 0.09 \n", + "509 1 0.30 \n", + "\n", + " 1 prob lipinski \n", + "347 0.96 True \n", + "615 1.00 True \n", + "106 0.99 True \n", + "757 0.91 True \n", + "509 0.70 True " + ] + }, + "execution_count": 348, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a last filtering step, we look at the results from the random forest again. In addition to a classification results, we can also look at the classification probability. We want to only keep compounds with a high probability of being classified as active (1). \n" + ] + }, + { + "cell_type": "code", + "execution_count": 349, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(11, 8)" + ] + }, + "execution_count": 349, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# filter with RF probability\n", + "cluster_df = cluster_df[cluster_df[\"1 prob\"] > 0.95]\n", + "cluster_df.shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can display our chosen compounds. " + ] + }, + { + "cell_type": "code", + "execution_count": 350, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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chembl_idcanonical_smilesmorganmoleculeactive/inactive0 prob1 problipinski
347CHEMBL2447950Cl.CCOc1cc2ncnc(Nc3cccc(Cl)c3)c2cc1OCC[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d7622c0>10.04000.9600True
615CHEMBL3671504Fc1cc(Br)ccc1Nc2ncnc3cc(OCCC=C)c(NC(=O)C=C)cc23[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d76ec80>10.00001.0000True
106CHEMBL138940Fc1ccc(Nc2ncnc3cnc(NC(=O)\\C=C\\C(=O)NCCCN4CCOCC...[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d756f20>10.01000.9900True
919CHEMBL513208CN(C)Cc1ccc(Nc2nc3ncnc(Nc4ccc(F)c(Cl)c4)c3s2)cc1[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d77d180>10.02000.9800True
765CHEMBL3952157COc1ccc(cc1)c2oc3ncnc(N[C@H](CO)c4ccccc4)c3c2[0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ...<rdkit.Chem.rdchem.Mol object at 0x18d775d80>10.01750.9825True
\n", + "
" + ], + "text/plain": [ + " chembl_id canonical_smiles \n", + "347 CHEMBL2447950 Cl.CCOc1cc2ncnc(Nc3cccc(Cl)c3)c2cc1OCC \\\n", + "615 CHEMBL3671504 Fc1cc(Br)ccc1Nc2ncnc3cc(OCCC=C)c(NC(=O)C=C)cc23 \n", + "106 CHEMBL138940 Fc1ccc(Nc2ncnc3cnc(NC(=O)\\C=C\\C(=O)NCCCN4CCOCC... \n", + "919 CHEMBL513208 CN(C)Cc1ccc(Nc2nc3ncnc(Nc4ccc(F)c(Cl)c4)c3s2)cc1 \n", + "765 CHEMBL3952157 COc1ccc(cc1)c2oc3ncnc(N[C@H](CO)c4ccccc4)c3c2 \n", + "\n", + " morgan \n", + "347 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \\\n", + "615 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, ... \n", + "106 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "919 [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "765 [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, ... \n", + "\n", + " molecule active/inactive 0 prob \n", + "347 1 0.0400 \\\n", + "615 1 0.0000 \n", + "106 1 0.0100 \n", + "919 1 0.0200 \n", + "765 1 0.0175 \n", + "\n", + " 1 prob lipinski \n", + "347 0.9600 True \n", + "615 1.0000 True \n", + "106 0.9900 True \n", + "919 0.9800 True \n", + "765 0.9825 True " + ] + }, + "execution_count": 351, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster_df.head()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To continue with the structure-based pipeline, we need to write the molecules to a file, so we have our final compounds. " + ] + }, + { + "cell_type": "code", + "execution_count": 352, + "metadata": {}, + "outputs": [], + "source": [ + "# write to file, we only need the chembl id and the smiles string \n", + "cluster_df.to_csv('data/filtered_compounds.csv', columns=['chembl_id', 'canonical_smiles'], index=False)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "py310_aisem2023", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +}