Skip to content

Commit

Permalink
Creato con Colaboratory
Browse files Browse the repository at this point in the history
  • Loading branch information
@francescopatane96
francescopatane96 committed Sep 16, 2022
1 parent c16ad65 commit 4b13992
Showing 1 changed file with 310 additions and 0 deletions.
310 changes: 310 additions & 0 deletions ML_3.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,310 @@
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"authorship_tag": "ABX9TyM1dCOzGN9zMcL1Nj+fE3Vk",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/francescopatane96/Computer_aided_drug_discovery_kit/blob/main/ML_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"source": [
"Unwanted substructures:\n",
"substructures can be reactive or toxic or they can interfere with certain assays. Filtering unwanted substructures can support assembling more efficient screening libraries, which can save time and resources.\n",
"\n",
"Examples of such unwanted features are nitro groups (mutagenic), sulfates and phosphates (likely resulting in unfavorable pharmacokinetic properties), 2-halopyridines and thiols (reactive). \n",
"\n",
"Pan Assay Interference Compounds (PAINS):\n",
"PAINS are compounds that often occur as hits in HTS even though they actually are false positives. PAINS show activity at numerous targets rather than one specific target."
],
"metadata": {
"id": "pXWQZuaa19_j"
}
},
{
"cell_type": "code",
"source": [
"!pip install rdkit"
],
"metadata": {
"id": "5Ebl1uvs2Vzz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "eYx9Zu8n0-5g"
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"from tqdm.auto import tqdm\n",
"from rdkit import Chem\n",
"from rdkit.Chem import PandasTools\n",
"from rdkit.Chem.FilterCatalog import FilterCatalog, FilterCatalogParams"
]
},
{
"cell_type": "code",
"source": [
"# load data from Talktorial T2\n",
"TNFB_data = pd.read_csv(\n",
" \"TNFB_compounds_lipinski.csv\",\n",
" index_col=0,\n",
")\n",
"# Drop unnecessary information\n",
"print(\"Dataframe shape:\", TNFB_data.shape)\n",
"TNFB_data.drop(columns=[\"molecular_weight\", \"n_hbd\", \"n_hba\", \"logp\"], inplace=True)\n",
"TNFB_data.head()"
],
"metadata": {
"id": "5wN1gfYL2fEr"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# Add molecule column\n",
"PandasTools.AddMoleculeColumnToFrame(TNFB_data, smilesCol=\"smiles\")\n",
"# Draw first 3 molecules\n",
"Chem.Draw.MolsToGridImage(\n",
" list(TNFB_data.head(3).ROMol),\n",
" legends=list(TNFB_data.head(3).molecule_chembl_id),\n",
")"
],
"metadata": {
"id": "rzsqw1ha3Ha8"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Filter for PAINS"
],
"metadata": {
"id": "u2dRkR2R3epc"
}
},
{
"cell_type": "code",
"source": [
"# initialize filter\n",
"params = FilterCatalogParams()\n",
"params.AddCatalog(FilterCatalogParams.FilterCatalogs.PAINS)\n",
"catalog = FilterCatalog(params)"
],
"metadata": {
"id": "mtlZeOxU3hJj"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# search for PAINS\n",
"matches = []\n",
"clean = []\n",
"for index, row in tqdm(TNFB_data.iterrows(), total=TNFB_data.shape[0]):\n",
" molecule = Chem.MolFromSmiles(row.smiles)\n",
" entry = catalog.GetFirstMatch(molecule) # Get the first matching PAINS\n",
" if entry is not None:\n",
" # store PAINS information\n",
" matches.append(\n",
" {\n",
" \"chembl_id\": row.molecule_chembl_id,\n",
" \"rdkit_molecule\": molecule,\n",
" \"pains\": entry.GetDescription().capitalize(),\n",
" }\n",
" )\n",
" else:\n",
" # collect indices of molecules without PAINS\n",
" clean.append(index)\n",
"\n",
"matches = pd.DataFrame(matches)\n",
"TNFB_data = TNFB_data.loc[clean] # keep molecules without PAINS"
],
"metadata": {
"id": "X2kiOCOo3oe7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(f\"Number of compounds with PAINS: {len(matches)}\")\n",
"print(f\"Number of compounds without PAINS: {len(TNFB_data)}\")"
],
"metadata": {
"id": "msNTp6h_3_g6"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"Chem.Draw.MolsToGridImage(\n",
" list(matches.head(5).rdkit_molecule),\n",
" legends=list(matches.head(5)[\"pains\"]),\n",
")"
],
"metadata": {
"id": "zAsR5xdo4Hy7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Filter and highlight unwanted substructures"
],
"metadata": {
"id": "hNbb8xcZ4ZCa"
}
},
{
"cell_type": "code",
"source": [
"substructures = pd.read_csv(\"unwantedSubstructures.csv\", sep=\"\\s+\")\n",
"substructures[\"rdkit_molecule\"] = substructures.smart.apply(Chem.MolFromSmarts)\n",
"print(\"Number of unwanted substructures in collection:\", len(substructures))"
],
"metadata": {
"id": "Zl3Lpz_Q4b3K"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"Chem.Draw.MolsToGridImage(\n",
" mols=substructures.rdkit_molecule.tolist()[2:5],\n",
" \n",
")"
],
"metadata": {
"id": "8XY7inLq7j5L"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# search for unwanted substructure\n",
"matches = []\n",
"clean = []\n",
"for index, row in tqdm(TNFB_data.iterrows(), total=TNFB_data.shape[0]):\n",
" molecule = Chem.MolFromSmiles(row.smiles)\n",
" match = False\n",
" for _, substructure in substructures.iterrows():\n",
" if molecule.HasSubstructMatch(substructure.rdkit_molecule):\n",
" matches.append(\n",
" {\n",
" \"chembl_id\": row.molecule_chembl_id,\n",
" \"rdkit_molecule\": molecule,\n",
" \"substructure\": substructure.rdkit_molecule,\n",
" \n",
" }\n",
" )\n",
" match = True\n",
" if not match:\n",
" clean.append(index)\n",
"\n",
"matches = pd.DataFrame(matches)\n",
"TNFB_data = TNFB_data.loc[clean]"
],
"metadata": {
"id": "pvqK_Sgq946c"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(f\"Number of found unwanted substructure: {len(matches)}\")\n",
"print(f\"Number of compounds without unwanted substructure: {len(TNFB_data)}\")"
],
"metadata": {
"id": "0AP-wfa4-RxL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"to_highlight = [\n",
" row.rdkit_molecule.GetSubstructMatch(row.substructure) for _, row in matches.head(3).iterrows()\n",
"]\n",
"Chem.Draw.MolsToGridImage(\n",
" list(matches.head(3).rdkit_molecule),\n",
" highlightAtomLists=to_highlight,\n",
" \n",
")"
],
"metadata": {
"id": "46RQc5Mg-cSS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Substructure statistics"
],
"metadata": {
"id": "vHbNd3uc-may"
}
},
{
"cell_type": "code",
"source": [
"\n",
"group_frequencies = groups.size()\n",
"group_frequencies.sort_values(ascending=False, inplace=True)\n",
"group_frequencies.head(10)"
],
"metadata": {
"id": "XEZb6VBj-myq"
},
"execution_count": null,
"outputs": []
}
]
}

0 comments on commit 4b13992

Please sign in to comment.