first add
This commit is contained in:
mm644706215
1
.gitignore
vendored
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1
.gitignore
vendored
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test/
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1
assets/ring16.smi
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1
assets/ring16.smi
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CC[C@H]1OC(=O)[*][*sugar*][C@H](C)[*sugar*][*]C[*]C(=O)[*][C@@H](O)[*]1
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40
docker/Dockerfile
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40
docker/Dockerfile
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FROM mambaorg/micromamba:cuda12.4.1-ubuntu22.04
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ARG ROOT_PASSWD="micromamba"
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ENV ROOT_PASSWD=${ROOT_PASSWD}
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USER root
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RUN <<EOT
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#!/bin/bash
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apt update
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apt install vim git sudo bash-completion wget curl htop jq bzip2 npm unzip libz-dev libgomp1 libblas-dev liblapack-dev libzmq3-dev -y
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echo "mambauser ALL=(ALL) NOPASSWD: ALL" | sudo tee -a /etc/sudoers > /dev/null
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echo "mambauser:${ROOT_PASSWD}" | chpasswd
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EOT
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USER mambauser
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ENV LILLYMOL_HOME=/home/mambauser/LillyMol \
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BUILD_DIR=Linux \
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BUILD_BDB=1 \
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BUILD_PYTHON=1
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RUN <<EOT
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#!/bin/bash
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echo "alias conda=micromamba" >> /home/mambauser/.bashrc
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echo "alias mamba=micromamba" >> /home/mambauser/.bashrc
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micromamba create -n lillymol python=3.10 pybind11 absl-py protobuf pandas scipy -y
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echo "mamba activate lillymol" >> /home/mambauser/.bashrc
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git clone https://github.com/IanAWatson/LillyMol /home/mambauser/LillyMol
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EOT
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WORKDIR /home/mambauser/LillyMol
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RUN <<EOT
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#!/bin/bash
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npm install -g @bazel/bazelisk
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cd src
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bash ./build_third_party.sh
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python update_python_in_workspace.py /home/mambauser/LillyMol/src/WORKSPACE
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bazelisk -h
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make all
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EOT
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# /home/mambauser/LillyMol/src/Molecule_Lib/linear_fingerprint_test.cc
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# line 373 EXPECT_EQ(_sfc.nbits(), 5l);
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14
docker/docker-compose.yml
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14
docker/docker-compose.yml
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version: '3'
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services:
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app:
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build:
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context: .
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dockerfile: Dockerfile
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args:
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CUDA_VERSION: 12.4
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image: hotwa/lillymol:20250213
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container_name: lilly_mol
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volumes:
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- ../test:/LillyMol/test
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command: >
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bash -c "tail -f /dev/null"
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1136
utils/main.ipynb
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1136
utils/main.ipynb
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File diff suppressed because one or more lines are too long
83
utils/ring_analysis.py
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83
utils/ring_analysis.py
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from rdkit import Chem
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from joblib import Parallel, delayed
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import logging
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from collections import Counter
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# 定义日志配置
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logging.basicConfig(
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filename="rgroup_matching.log",
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level=logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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)
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# 定义 SMARTS 模式
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macro = Chem.MolFromSmarts("[r12,r13,r14,r15,r16,r17,r18,r19,r20]([#8][#6](=[#8]))")
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# 读取 SMI 文件
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smi_file = "/home/mambauser/LillyMol/test/1M_stratsampled_V1B.smi"
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with open(smi_file, 'r') as f:
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SMILES_list = [line.strip() for line in f if line.strip()]
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logging.info(f"Loaded {len(SMILES_list)} molecules from {smi_file}.")
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# 匹配和最大环统计函数
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def match_and_ring_analysis(smiles):
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mol = Chem.MolFromSmiles(smiles)
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if mol is None:
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return smiles, None, 0 # 无效分子
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result = mol.GetSubstructMatches(macro)
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ri = mol.GetRingInfo()
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largest_ring_size = max((len(r) for r in ri.AtomRings()), default=0)
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return smiles, result, largest_ring_size
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# 使用 joblib 并行处理
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logging.info("Starting SMARTS matching and ring analysis...")
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results = Parallel(n_jobs=-1)(delayed(match_and_ring_analysis)(s) for s in SMILES_list)
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# 分离成功、失败的分子以及最大环大小
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success = [smiles for smiles, result, _ in results if result]
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fail = [smiles for smiles, result, _ in results if not result]
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ring_sizes = [largest_ring_size for _, _, largest_ring_size in results]
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# 统计最大环频数
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ring_size_counter = Counter(ring_sizes)
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# 统计结果
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total = len(success) + len(fail)
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success_rate = len(success) / total * 100 if total > 0 else 0
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# 保存日志信息
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logging.info(f"Total molecules: {total}")
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logging.info(f"Success: {len(success)}")
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logging.info(f"Fail: {len(fail)}")
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logging.info(f"Success rate: {success_rate:.2f}%")
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logging.info(f"Ring size distribution: {dict(ring_size_counter)}")
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print(f"Total molecules: {total}")
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print(f"Success: {len(success)}")
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print(f"Fail: {len(fail)}")
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print(f"Success rate: {success_rate:.2f}%")
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print("Ring size distribution:")
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for size, count in sorted(ring_size_counter.items()):
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print(f" Ring size {size}: {count}")
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# 将失败的分子写入到一个 SMI 文件
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fail_smi_file = "fail_molecules.smi"
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with open(fail_smi_file, "w") as ff:
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for smiles in fail:
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ff.write(smiles + "\n")
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logging.info(f"Failed molecules written to {fail_smi_file}.")
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print(f"Failed molecules written to {fail_smi_file}.")
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# 将环大小分布写入文件
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ring_size_file = "ring_size_distribution.txt"
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with open(ring_size_file, "w") as rf:
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rf.write("Ring Size\tCount\n")
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for size, count in sorted(ring_size_counter.items()):
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rf.write(f"{size}\t{count}\n")
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logging.info(f"Ring size distribution written to {ring_size_file}.")
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print(f"Ring size distribution written to {ring_size_file}.")
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182
utils/search.ipynb
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182
utils/search.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "34ca5db2",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/root/micromamba/envs/qsar/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ChEMBL 数据库存储路径: /srv/project/LillyMol/test/data/35/data/chembl_35/chembl_35_sqlite/chembl_35.db\n"
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]
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}
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],
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"source": [
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"import chembl_downloader\n",
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"# 指定存储目录\n",
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"prefix = '/srv/project/LillyMol/test/data'\n",
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"version = chembl_downloader.latest() # version 35\n",
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"\n",
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"# 下载并提取 ChEMBL 数据库，存放到指定目录\n",
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"path = chembl_downloader.download_extract_sqlite(version=version, prefix=[prefix])\n",
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"# chembl_downloader.download_sqlite(version)\n",
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"\n",
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"print(f\"ChEMBL 数据库存储路径: {path}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "dd6f3b25",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"(0, 'molregno', 'BIGINT', 1, None, 0)\n",
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"(1, 'syn_type', 'VARCHAR(50)', 1, None, 0)\n",
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"(2, 'molsyn_id', 'BIGINT', 1, None, 1)\n",
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"(3, 'res_stem_id', 'BIGINT', 0, None, 0)\n",
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"(4, 'synonyms', 'VARCHAR(250)', 0, None, 0)\n"
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]
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}
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],
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"source": [
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"import chembl_downloader\n",
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"\n",
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"with chembl_downloader.connect(version='35', prefix=['/srv/project/LillyMol/test/data']) as conn:\n",
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" cursor = conn.cursor()\n",
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" cursor.execute(\"PRAGMA table_info(molecule_synonyms);\")\n",
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" columns = cursor.fetchall()\n",
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" for col in columns:\n",
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" print(col)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "c8390d91",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>molregno</th>\n",
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" <th>syn_type</th>\n",
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" <th>molsyn_id</th>\n",
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" <th>res_stem_id</th>\n",
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" <th>synonyms</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>27340</td>\n",
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" <td>TRADE_NAME</td>\n",
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" <td>3001861</td>\n",
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" <td>None</td>\n",
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" <td>Brolene antibiotic</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>27340</td>\n",
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" <td>TRADE_NAME</td>\n",
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" <td>3001879</td>\n",
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" <td>None</td>\n",
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" <td>Golden eye antibiotic</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" molregno syn_type molsyn_id res_stem_id synonyms\n",
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"0 27340 TRADE_NAME 3001861 None Brolene antibiotic\n",
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"1 27340 TRADE_NAME 3001879 None Golden eye antibiotic"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import chembl_downloader\n",
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"\n",
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"sql = \"\"\"\n",
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"SELECT *\n",
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"FROM molecule_synonyms\n",
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"WHERE syn_type='TRADE_NAME' AND synonyms LIKE '%antibiotic%'\n",
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"\"\"\"\n",
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"\n",
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"\n",
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"# version 一般也建议加上，保证和你本地一致\n",
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"df = chembl_downloader.query(\n",
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" sql,\n",
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" version='35', # 或者 version=version\n",
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" prefix=['/srv/project/LillyMol/test/data']\n",
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")\n",
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"df\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d0c56718",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "qsar",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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17
utils/search.py
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17
utils/search.py
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import sqlite3
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# 连接到数据库
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conn = sqlite3.connect('../test/data/35/data/chembl_35/chembl_35_sqlite/chembl_35.db')
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# 查询抗生素相关分子 (修正版)
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query = """
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SELECT m.chembl_id, m.pref_name, cs.molecule_synonym
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FROM molecule_synonyms cs
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JOIN molecule_dictionary m ON cs.molregno = m.molregno
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WHERE cs.molecule_synonym LIKE '%antibiotic%'
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LIMIT 10
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"""
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cursor = conn.execute(query)
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for row in cursor.fetchall():
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print(row)
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60
utils/test_smart.py
Executable file
60
utils/test_smart.py
Executable file
@@ -0,0 +1,60 @@
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from rdkit import Chem
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from joblib import Parallel, delayed
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import logging
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# 定义日志配置
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logging.basicConfig(
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filename="rgroup_matching.log",
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level=logging.INFO,
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format="%(asctime)s - %(levelname)s - %(message)s",
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)
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# 定义 SMARTS 模式
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macro = Chem.MolFromSmarts("[r12,r13,r14,r15,r16,r17,r18,r19,r20]([#8][#6](=[#8]))")
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# 读取 SMI 文件
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smi_file = "/home/mambauser/LillyMol/test/1M_stratsampled_V1B.smi"
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with open(smi_file, 'r') as f:
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SMILES_list = [line.strip() for line in f if line.strip()]
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logging.info(f"Loaded {len(SMILES_list)} molecules from {smi_file}.")
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# 匹配函数
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def match_smarts(smiles):
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mol = Chem.MolFromSmiles(smiles)
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if mol is None:
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return smiles, None # 无效分子
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result = mol.GetSubstructMatches(macro)
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return smiles, result
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# 使用 joblib 并行处理
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logging.info("Starting SMARTS matching...")
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results = Parallel(n_jobs=-1)(delayed(match_smarts)(s) for s in SMILES_list)
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# 分离成功和失败的分子
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success = [smiles for smiles, result in results if result]
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fail = [smiles for smiles, result in results if not result]
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# 统计结果
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total = len(success) + len(fail)
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success_rate = len(success) / total * 100 if total > 0 else 0
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# 保存日志信息
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logging.info(f"Total molecules: {total}")
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logging.info(f"Success: {len(success)}")
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logging.info(f"Fail: {len(fail)}")
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logging.info(f"Success rate: {success_rate:.2f}%")
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print(f"Total molecules: {total}")
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print(f"Success: {len(success)}")
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print(f"Fail: {len(fail)}")
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print(f"Success rate: {success_rate:.2f}%")
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# 将失败的分子写入到一个 SMI 文件
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fail_smi_file = "fail_molecules.smi"
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with open(fail_smi_file, "w") as ff:
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for smiles in fail:
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ff.write(smiles + "\n")
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logging.info(f"Failed molecules written to {fail_smi_file}.")
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print(f"Failed molecules written to {fail_smi_file}.")
|
||||
Reference in New Issue
Block a user