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mm644706215
2025-09-17 22:14:05 +08:00
commit 64d80d2e3c
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.gitignore vendored Normal file
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test/

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assets/ring16.smi Executable file
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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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docker/Dockerfile Executable file
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FROM mambaorg/micromamba:cuda12.4.1-ubuntu22.04
ARG ROOT_PASSWD="micromamba"
ENV ROOT_PASSWD=${ROOT_PASSWD}
USER root
RUN <<EOT
#!/bin/bash
apt update
apt install vim git sudo bash-completion wget curl htop jq bzip2 npm unzip libz-dev libgomp1 libblas-dev liblapack-dev libzmq3-dev -y
echo "mambauser ALL=(ALL) NOPASSWD: ALL" | sudo tee -a /etc/sudoers > /dev/null
echo "mambauser:${ROOT_PASSWD}" | chpasswd
EOT
USER mambauser
ENV LILLYMOL_HOME=/home/mambauser/LillyMol \
BUILD_DIR=Linux \
BUILD_BDB=1 \
BUILD_PYTHON=1
RUN <<EOT
#!/bin/bash
echo "alias conda=micromamba" >> /home/mambauser/.bashrc
echo "alias mamba=micromamba" >> /home/mambauser/.bashrc
micromamba create -n lillymol python=3.10 pybind11 absl-py protobuf pandas scipy -y
echo "mamba activate lillymol" >> /home/mambauser/.bashrc
git clone https://github.com/IanAWatson/LillyMol /home/mambauser/LillyMol
EOT
WORKDIR /home/mambauser/LillyMol
RUN <<EOT
#!/bin/bash
npm install -g @bazel/bazelisk
cd src
bash ./build_third_party.sh
python update_python_in_workspace.py /home/mambauser/LillyMol/src/WORKSPACE
bazelisk -h
make all
EOT
# /home/mambauser/LillyMol/src/Molecule_Lib/linear_fingerprint_test.cc
# line 373 EXPECT_EQ(_sfc.nbits(), 5l);

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docker/docker-compose.yml Executable file
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version: '3'
services:
app:
build:
context: .
dockerfile: Dockerfile
args:
CUDA_VERSION: 12.4
image: hotwa/lillymol:20250213
container_name: lilly_mol
volumes:
- ../test:/LillyMol/test
command: >
bash -c "tail -f /dev/null"

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utils/main.ipynb Executable file
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utils/ring_analysis.py Executable file
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from rdkit import Chem
from joblib import Parallel, delayed
import logging
from collections import Counter
# 定义日志配置
logging.basicConfig(
filename="rgroup_matching.log",
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
# 定义 SMARTS 模式
macro = Chem.MolFromSmarts("[r12,r13,r14,r15,r16,r17,r18,r19,r20]([#8][#6](=[#8]))")
# 读取 SMI 文件
smi_file = "/home/mambauser/LillyMol/test/1M_stratsampled_V1B.smi"
with open(smi_file, 'r') as f:
SMILES_list = [line.strip() for line in f if line.strip()]
logging.info(f"Loaded {len(SMILES_list)} molecules from {smi_file}.")
# 匹配和最大环统计函数
def match_and_ring_analysis(smiles):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return smiles, None, 0 # 无效分子
result = mol.GetSubstructMatches(macro)
ri = mol.GetRingInfo()
largest_ring_size = max((len(r) for r in ri.AtomRings()), default=0)
return smiles, result, largest_ring_size
# 使用 joblib 并行处理
logging.info("Starting SMARTS matching and ring analysis...")
results = Parallel(n_jobs=-1)(delayed(match_and_ring_analysis)(s) for s in SMILES_list)
# 分离成功、失败的分子以及最大环大小
success = [smiles for smiles, result, _ in results if result]
fail = [smiles for smiles, result, _ in results if not result]
ring_sizes = [largest_ring_size for _, _, largest_ring_size in results]
# 统计最大环频数
ring_size_counter = Counter(ring_sizes)
# 统计结果
total = len(success) + len(fail)
success_rate = len(success) / total * 100 if total > 0 else 0
# 保存日志信息
logging.info(f"Total molecules: {total}")
logging.info(f"Success: {len(success)}")
logging.info(f"Fail: {len(fail)}")
logging.info(f"Success rate: {success_rate:.2f}%")
logging.info(f"Ring size distribution: {dict(ring_size_counter)}")
print(f"Total molecules: {total}")
print(f"Success: {len(success)}")
print(f"Fail: {len(fail)}")
print(f"Success rate: {success_rate:.2f}%")
print("Ring size distribution:")
for size, count in sorted(ring_size_counter.items()):
print(f" Ring size {size}: {count}")
# 将失败的分子写入到一个 SMI 文件
fail_smi_file = "fail_molecules.smi"
with open(fail_smi_file, "w") as ff:
for smiles in fail:
ff.write(smiles + "\n")
logging.info(f"Failed molecules written to {fail_smi_file}.")
print(f"Failed molecules written to {fail_smi_file}.")
# 将环大小分布写入文件
ring_size_file = "ring_size_distribution.txt"
with open(ring_size_file, "w") as rf:
rf.write("Ring Size\tCount\n")
for size, count in sorted(ring_size_counter.items()):
rf.write(f"{size}\t{count}\n")
logging.info(f"Ring size distribution written to {ring_size_file}.")
print(f"Ring size distribution written to {ring_size_file}.")

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utils/search.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "34ca5db2",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/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",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ChEMBL 数据库存储路径: /srv/project/LillyMol/test/data/35/data/chembl_35/chembl_35_sqlite/chembl_35.db\n"
]
}
],
"source": [
"import chembl_downloader\n",
"# 指定存储目录\n",
"prefix = '/srv/project/LillyMol/test/data'\n",
"version = chembl_downloader.latest() # version 35\n",
"\n",
"# 下载并提取 ChEMBL 数据库，存放到指定目录\n",
"path = chembl_downloader.download_extract_sqlite(version=version, prefix=[prefix])\n",
"# chembl_downloader.download_sqlite(version)\n",
"\n",
"print(f\"ChEMBL 数据库存储路径: {path}\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dd6f3b25",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(0, 'molregno', 'BIGINT', 1, None, 0)\n",
"(1, 'syn_type', 'VARCHAR(50)', 1, None, 0)\n",
"(2, 'molsyn_id', 'BIGINT', 1, None, 1)\n",
"(3, 'res_stem_id', 'BIGINT', 0, None, 0)\n",
"(4, 'synonyms', 'VARCHAR(250)', 0, None, 0)\n"
]
}
],
"source": [
"import chembl_downloader\n",
"\n",
"with chembl_downloader.connect(version='35', prefix=['/srv/project/LillyMol/test/data']) as conn:\n",
" cursor = conn.cursor()\n",
" cursor.execute(\"PRAGMA table_info(molecule_synonyms);\")\n",
" columns = cursor.fetchall()\n",
" for col in columns:\n",
" print(col)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c8390d91",
"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>molregno</th>\n",
" <th>syn_type</th>\n",
" <th>molsyn_id</th>\n",
" <th>res_stem_id</th>\n",
" <th>synonyms</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>27340</td>\n",
" <td>TRADE_NAME</td>\n",
" <td>3001861</td>\n",
" <td>None</td>\n",
" <td>Brolene antibiotic</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>27340</td>\n",
" <td>TRADE_NAME</td>\n",
" <td>3001879</td>\n",
" <td>None</td>\n",
" <td>Golden eye antibiotic</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" molregno syn_type molsyn_id res_stem_id synonyms\n",
"0 27340 TRADE_NAME 3001861 None Brolene antibiotic\n",
"1 27340 TRADE_NAME 3001879 None Golden eye antibiotic"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import chembl_downloader\n",
"\n",
"sql = \"\"\"\n",
"SELECT *\n",
"FROM molecule_synonyms\n",
"WHERE syn_type='TRADE_NAME' AND synonyms LIKE '%antibiotic%'\n",
"\"\"\"\n",
"\n",
"\n",
"# version 一般也建议加上，保证和你本地一致\n",
"df = chembl_downloader.query(\n",
" sql,\n",
" version='35', # 或者 version=version\n",
" prefix=['/srv/project/LillyMol/test/data']\n",
")\n",
"df\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d0c56718",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "qsar",
"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": "3.11.11"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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utils/search.py Normal file
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import sqlite3
# 连接到数据库
conn = sqlite3.connect('../test/data/35/data/chembl_35/chembl_35_sqlite/chembl_35.db')
# 查询抗生素相关分子 (修正版)
query = """
SELECT m.chembl_id, m.pref_name, cs.molecule_synonym
FROM molecule_synonyms cs
JOIN molecule_dictionary m ON cs.molregno = m.molregno
WHERE cs.molecule_synonym LIKE '%antibiotic%'
LIMIT 10
"""
cursor = conn.execute(query)
for row in cursor.fetchall():
print(row)

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utils/test_smart.py Executable file
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from rdkit import Chem
from joblib import Parallel, delayed
import logging
# 定义日志配置
logging.basicConfig(
filename="rgroup_matching.log",
level=logging.INFO,
format="%(asctime)s - %(levelname)s - %(message)s",
)
# 定义 SMARTS 模式
macro = Chem.MolFromSmarts("[r12,r13,r14,r15,r16,r17,r18,r19,r20]([#8][#6](=[#8]))")
# 读取 SMI 文件
smi_file = "/home/mambauser/LillyMol/test/1M_stratsampled_V1B.smi"
with open(smi_file, 'r') as f:
SMILES_list = [line.strip() for line in f if line.strip()]
logging.info(f"Loaded {len(SMILES_list)} molecules from {smi_file}.")
# 匹配函数
def match_smarts(smiles):
mol = Chem.MolFromSmiles(smiles)
if mol is None:
return smiles, None # 无效分子
result = mol.GetSubstructMatches(macro)
return smiles, result
# 使用 joblib 并行处理
logging.info("Starting SMARTS matching...")
results = Parallel(n_jobs=-1)(delayed(match_smarts)(s) for s in SMILES_list)
# 分离成功和失败的分子
success = [smiles for smiles, result in results if result]
fail = [smiles for smiles, result in results if not result]
# 统计结果
total = len(success) + len(fail)
success_rate = len(success) / total * 100 if total > 0 else 0
# 保存日志信息
logging.info(f"Total molecules: {total}")
logging.info(f"Success: {len(success)}")
logging.info(f"Fail: {len(fail)}")
logging.info(f"Success rate: {success_rate:.2f}%")
print(f"Total molecules: {total}")
print(f"Success: {len(success)}")
print(f"Fail: {len(fail)}")
print(f"Success rate: {success_rate:.2f}%")
# 将失败的分子写入到一个 SMI 文件
fail_smi_file = "fail_molecules.smi"
with open(fail_smi_file, "w") as ff:
for smiles in fail:
ff.write(smiles + "\n")
logging.info(f"Failed molecules written to {fail_smi_file}.")
print(f"Failed molecules written to {fail_smi_file}.")