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PatWalters/bitbirch_test.ipynb

Last active August 23, 2024 23:10
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bitbirch_test.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "7f839d17-fa63-4698-ad05-c9c201ffa358",
"metadata": {},
"source": [
"This notebook provides a quick demo of the clustering method described in [Efficient clustering of large molecular libraries](https://www.biorxiv.org/content/10.1101/2024.08.10.607459v1) by Vicky Jung, Kenneth Lopez Perez, Lexin Chen, Kate Huddleston, and Ramon Alain Miranda Quintana. To use this notebook, first clone the authors' git repo. \n",
"<pre>\n",
"git clone [email protected]:mqcomplab/bitbirch.git\n",
"</pre>"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "3e4f9931",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from rdkit import Chem, DataStructs\n",
"from rdkit.Chem import rdFingerprintGenerator\n",
"from bitbirch import BitBirch\n",
"import numpy as np\n",
"import mols2grid\n",
"from tqdm.auto import tqdm\n",
"from rdkit.Chem.Descriptors import MolWt\n",
"import seaborn as sns"
]
},
{
"cell_type": "markdown",
"id": "4ca1c9e9-887a-42b8-9ec7-cb22a8dd2b96",
"metadata": {},
"source": [
"Enable progress bars with Pandas **progress_apply**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "94aa3591-4e75-45a9-bf41-6355f7102fb5",
"metadata": {},
"outputs": [],
"source": [
"tqdm.pandas()"
]
},
{
"cell_type": "markdown",
"id": "6a9587fc-d172-4e45-9aee-79f5db27251f",
"metadata": {},
"source": [
"This notebook uses chembl_34_chemreps.txt.gz, which can be downloaded [here](https://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/chembl_34_chemreps.txt.gz)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "69a60248",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 7.97 s, sys: 450 ms, total: 8.42 s\n",
"Wall time: 8.62 s\n"
]
},
{
"data": {
"text/plain": [
"2.40927"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"df = pd.read_csv(\"chembl_34_chemreps.txt.gz\",sep=\"\\t\")\n",
"len(df)/1e6"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a0d06e90-b923-48ad-9ab1-0916bf4770f2",
"metadata": {},
"outputs": [],
"source": [
"def smi2mw(smi):\n",
" mol = Chem.MolFromSmiles(smi)\n",
" if mol:\n",
" return MolWt(mol)\n",
" else:\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "f48ae2ff-3566-4e86-9db2-03bfeb09d8ff",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "09423de2e5594c11bae747b181f71161",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
" 0%| | 0/2409270 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[12:07:14] WARNING: not removing hydrogen atom without neighbors\n",
"[12:07:38] WARNING: not removing hydrogen atom without neighbors\n",
"[12:07:38] WARNING: not removing hydrogen atom without neighbors\n",
"[12:08:28] WARNING: not removing hydrogen atom without neighbors\n",
"[12:08:48] WARNING: not removing hydrogen atom without neighbors\n",
"[12:08:50] WARNING: not removing hydrogen atom without neighbors\n",
"[12:08:52] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:22] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:22] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:22] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:25] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5 6 10 11 15 16 17 19 20 21\n",
"[12:09:34] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:34] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:34] WARNING: not removing hydrogen atom without neighbors\n",
"[12:09:34] WARNING: not removing hydrogen atom without neighbors\n"
]
}
],
"source": [
"df['MW'] = df.canonical_smiles.progress_apply(smi2mw)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "46d1651f-5c3d-467e-b6f1-903fd4ecb785",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>chembl_id</th>\n",
" <th>canonical_smiles</th>\n",
" <th>standard_inchi</th>\n",
" <th>standard_inchi_key</th>\n",
" <th>MW</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>2311326</th>\n",
" <td>CHEMBL4080644</td>\n",
" <td>Cc1ccc2c(c1)-n1-c(=O)/c=c\\c(=O)-n-2-c2cc(C)ccc2-1</td>\n",
" <td>InChI=1S/C18H14N2O2/c1-11-3-5-13-15(9-11)19-14...</td>\n",
" <td>UZIXOQDBFHHCEC-FPLPWBNLSA-N</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" chembl_id canonical_smiles \\\n",
"2311326 CHEMBL4080644 Cc1ccc2c(c1)-n1-c(=O)/c=c\\c(=O)-n-2-c2cc(C)ccc2-1 \n",
"\n",
" standard_inchi \\\n",
"2311326 InChI=1S/C18H14N2O2/c1-11-3-5-13-15(9-11)19-14... \n",
"\n",
" standard_inchi_key MW \n",
"2311326 UZIXOQDBFHHCEC-FPLPWBNLSA-N NaN "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df.MW.isna()]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ab5baef1-155b-464f-929b-bd33a83bb690",
"metadata": {},
"outputs": [],
"source": [
"df.dropna(subset=\"MW\",inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "29a51254-79bf-4c2f-a709-5f3439cf82dc",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x38526cb10>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 500x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"g = sns.displot(df.MW)\n",
"g.set(xlim=(0,1000))"
]
},
{
"cell_type": "markdown",
"id": "6509e0b5-4c11-4035-88a1-7f3cbf20393c",
"metadata": {},
"source": [
"Remove molecules with MW > 500 from the dataframe. I did this mainly because I didn't want to look at clusters of very large molecules. "
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "0e2292dc-c1e9-4280-b1f9-ff66462474fd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1.931349"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = df.query(\"MW <= 500\")\n",
"len(df)/1e6"
]
},
{
"cell_type": "markdown",
"id": "33d37e89-3cce-4e7b-bd1a-c98c57986802",
"metadata": {},
"source": [
"Define a quick class to generate RDKit fingerprints from SMILES. It would have been more efficient to add an RDKit molecule column to the dataframe, but this takes up a lot of memory when the dataframe has 2M rows. "
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "7c2c25d4",
"metadata": {},
"outputs": [],
"source": [
"class SmiFpGen:\n",
" def __init__(self):\n",
" self.mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=2,fpSize=2048)\n",
" \n",
" def smi2numpy_fp(self,smi):\n",
" mol = Chem.MolFromSmiles(smi)\n",
" if mol:\n",
" return self.mfpgen.GetFingerprintAsNumPy(mol)\n",
" else:\n",
" return None"
]
},
{
"cell_type": "markdown",
"id": "603ca199-23ba-4d2b-a4b2-f1863b8721ee",
"metadata": {},
"source": [
"Instantiate a fingerprint generator"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "bee61037-622f-4f1d-823c-f18e884cbfb1",
"metadata": {},
"outputs": [],
"source": [
"smifpgen = SmiFpGen()"
]
},
{
"cell_type": "markdown",
"id": "1cb8aa42-7467-443f-bae6-1b3e3f0ac1b3",
"metadata": {},
"source": [
"Calculate fingerprints. In practice, it would probably be good to normalize structures, remove salts, etc. "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "49ad9592-7699-42e7-b419-7bf1d29a80ac",
"metadata": {},
"outputs": [
{
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" 0%| | 0/1931349 [00:00<?, ?it/s]"
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"text": [
"[12:21:23] WARNING: not removing hydrogen atom without neighbors\n",
"[12:21:23] WARNING: not removing hydrogen atom without neighbors\n",
"[12:22:20] WARNING: not removing hydrogen atom without neighbors\n",
"[12:22:39] WARNING: not removing hydrogen atom without neighbors\n",
"[12:22:41] WARNING: not removing hydrogen atom without neighbors\n",
"[12:22:43] WARNING: not removing hydrogen atom without neighbors\n"
]
},
{
"name": "stdout",
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"text": [
"CPU times: user 3min 14s, sys: 8.42 s, total: 3min 23s\n",
"Wall time: 3min 20s\n"
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}
],
"source": [
"%%time\n",
"df['numpy_fp'] = df.canonical_smiles.progress_apply(smifpgen.smi2numpy_fp)"
]
},
{
"cell_type": "markdown",
"id": "4cbb0bbd-be7b-421e-b5ae-8da33aeb8327",
"metadata": {},
"source": [
"Check to see if fingerprint generation failed for any of the ChEMBL molecules. "
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "cede294b-477f-4db2-b472-414660bf67c6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
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"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"sum(df.numpy_fp.isna())"
]
},
{
"cell_type": "markdown",
"id": "4cf7611b-ce21-4dac-a873-f2e597decb69",
"metadata": {},
"source": [
"Convert the fingerprints to int64"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9af04e1-306d-4dcb-a413-9084602c5d90",
"metadata": {},
"outputs": [],
"source": [
"fp64 = np.stack(df.numpy_fp).astype(np.int64)"
]
},
{
"cell_type": "markdown",
"id": "590c8d78-d73b-4b95-adb1-cf856e03bec3",
"metadata": {},
"source": [
"Instantiate a BitBirch object with default parameters"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "30d3bba3",
"metadata": {},
"outputs": [],
"source": [
"bb = BitBirch()"
]
},
{
"cell_type": "markdown",
"id": "6345b883-bf85-45a6-a391-b9197f498ae8",
"metadata": {},
"source": [
"Cluster with BitBirch"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "a915cf16",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/pwalters/software/bitbirch/bitbirch.py:49: RuntimeWarning: overflow encountered in scalar subtract\n",
" a = (sum_kqsq - sum_kq)/2\n",
"/Users/pwalters/software/bitbirch/bitbirch.py:83: RuntimeWarning: invalid value encountered in divide\n",
" sims_med = a_centroid / (pop_counts + np.sum(centroid) - a_centroid)\n",
"/Users/pwalters/software/bitbirch/bitbirch.py:90: RuntimeWarning: invalid value encountered in divide\n",
" sims_mol1 = a_mol1 / (pop_counts + pop_counts[mol1] - a_mol1)\n",
"/Users/pwalters/software/bitbirch/bitbirch.py:97: RuntimeWarning: invalid value encountered in divide\n",
" sims_mol2 = a_mol2 / (pop_counts + pop_counts[mol2] - a_mol2)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 6min 8s, sys: 7.51 s, total: 6min 16s\n",
"Wall time: 6min 26s\n"
]
},
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"data": {
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"execution_count": 30,
"metadata": {},
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"source": [
"%%time\n",
"bb.fit(np.stack(fp64))"
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{
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"id": "3e87c49b-6875-4087-8150-4998a0aacbf7",
"metadata": {},
"source": [
"BitBirch returns a list of lists with cluster membership. Turn this into a dataframe column with cluster ids. "
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "c11746a0",
"metadata": {},
"outputs": [],
"source": [
"cluster_id_list = np.repeat(-1,len(df))\n",
"for cluster_num,row in enumerate(bb.get_cluster_mol_ids()):\n",
" for r in row:\n",
" cluster_id_list[r] = cluster_num\n",
"df['cluster'] = cluster_id_list"
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},
{
"cell_type": "markdown",
"id": "9af8eafc-d18e-4ae6-b95f-cdf90b9d8fa0",
"metadata": {},
"source": [
"Examine the sizes of the clusters"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "7e781f45",
"metadata": {},
"outputs": [
{
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"... ... ...\n",
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"metadata": {},
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"source": [
"count_df = df.cluster.value_counts().to_frame()\n",
"count_df.index.name = \"cluster\"\n",
"count_df.columns = [\"count\"]\n",
"count_df.reset_index(inplace=True)\n",
"count_df"
]
},
{
"cell_type": "markdown",
"id": "0c049e4b-17a6-42e0-8c03-2a7366be0615",
"metadata": {},
"source": [
"How many clusters have at least 5 molecules? "
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "22c7aa33-e7ef-4031-819d-1ebff71e5730",
"metadata": {},
"outputs": [
{
"data": {
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"3997"
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},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"len(count_df.query(\"count >= 5\"))"
]
},
{
"cell_type": "markdown",
"id": "77e12041-648e-4dea-8eb9-ab6775586a06",
"metadata": {},
"source": [
"What fraction of the clusters are singletons? "
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "5db53e0c-b162-44eb-830b-c889a951e892",
"metadata": {},
"outputs": [
{
"data": {
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"0.30927316201221067"
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},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"len(count_df.query(\"count == 1\"))/len(count_df)"
]
},
{
"cell_type": "markdown",
"id": "80bb136a-22f5-4e53-97e4-7a3617a0a9d3",
"metadata": {},
"source": [
"Use mols2grid to look at cluster members. In the cell below **cluster_idx** is row id in **count_df**. Put differently, cluster_idx = 0, is the cluster with largest number of members, cluster_idx = 1 is the next largest, etc. "
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "2c1729a3",
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"text/plain": [
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]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cluster_idx = 0\n",
"cluster_num = count_df.cluster.values[cluster_idx]\n",
"cluster_df = df.query(\"cluster == @cluster_num\")\n",
"mols2grid.display(cluster_df,size=(150,150),template=\"static\",smiles_col=\"canonical_smiles\",prerender=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0f71573b",
"metadata": {},
"outputs": [],
"source": []
}
],
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}
},
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