From 7ac0e585996192139ee447cd2eb03bd0e055584a Mon Sep 17 00:00:00 2001 From: hotwa Date: Wed, 15 Oct 2025 20:14:12 +0800 Subject: [PATCH] update --- .dockerignore | 59 + .gitattributes | 2 + .gitignore | 4 + README.md | 141 + config/example.txt | 6 + docker/Dockerfile | 69 + docker/README.md | 201 ++ docker/docker-compose.yml | 70 + docker/docker.env.example | 12 + pixi.lock | 4345 ++++++++++++++++++++++++ pixi.toml | 15 + scripts/analyze_qed_mw_distribution.py | 551 +++ scripts/analyze_results.py | 151 + scripts/calculate_qed_values.py | 228 ++ scripts/example_api_usage.py | 30 + scripts/qed.py | 201 ++ 16 files changed, 6085 insertions(+) create mode 100644 .dockerignore create mode 100644 .gitattributes create mode 100644 .gitignore create mode 100644 README.md create mode 100644 config/example.txt create mode 100644 docker/Dockerfile create mode 100644 docker/README.md create mode 100644 docker/docker-compose.yml create mode 100644 docker/docker.env.example create mode 100644 pixi.lock create mode 100644 pixi.toml create mode 100644 scripts/analyze_qed_mw_distribution.py create mode 100644 scripts/analyze_results.py create mode 100644 scripts/calculate_qed_values.py create mode 100644 scripts/example_api_usage.py create mode 100644 scripts/qed.py diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..ddcf840 --- /dev/null +++ b/.dockerignore @@ -0,0 +1,59 @@ +# Git 相关 +.git +.gitignore +.gitattributes + +# Docker 相关 +docker/ +.dockerignore + +# Python 相关 +__pycache__/ +*.py[cod] +*$py.class +*.so +.Python +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# 数据文件 +data/ +results/ +output/ +*.pdb +*.sdf +*.mol2 +*.xyz + +# 日志文件 +*.log +logs/ + +# 临时文件 +*.tmp +*.temp +.DS_Store +Thumbs.db + +# IDE 相关 +.vscode/ +.idea/ +*.swp +*.swo + +# 系统文件 +.DS_Store +Thumbs.db + +# 文档 +*.md +docs/ + +# 测试文件 +test/ +tests/ +*_test.py +test_*.py diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..887a2c1 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,2 @@ +# SCM syntax highlighting & preventing 3-way merges +pixi.lock merge=binary linguist-language=YAML linguist-generated=true diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c9d2dbc --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +# pixi environments +.pixi/* +!.pixi/config.toml +bin/ \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..a628607 --- /dev/null +++ b/README.md @@ -0,0 +1,141 @@ +## 环境管理 (使用 pixi) + +### 安装 pixi + +```bash +# 安装 pixi +curl -fsSL https://pixi.sh/install.sh | bash + +# 重新加载 shell 配置 +source ~/.bashrc # 或 source ~/.zshrc +``` + +### 初始化项目环境 + +```bash +# 在项目目录中初始化 pixi 环境 +pixi init + +# 添加所需的包 +pixi add rdkit openbabel meeko + +# 激活环境 +pixi shell +``` + +### 使用环境 + +```bash +# 激活 pixi 环境 +pixi shell + +# 在环境中运行脚本 +pixi run python scripts/your_script.py + +# 或者直接使用 pixi 执行命令 +pixi run vina --help +``` + +## AutoDock Vina 安装 + +### 下载二进制文件 + +[Download](https://github.com/ccsb-scripps/AutoDock-Vina/releases/tag/v1.2.7) + +for macos: + +```bash + +wget -O ./bin/vina_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_1.2.7_mac_aarch64 +wget -O ./bin/vina_split_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_split_1.2.7_mac_aarch64 + +# 或者使用 curl: +curl -L -o ./bin/vina_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_1.2.7_mac_aarch64 +curl -L -o ./bin/vina_split_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_split_1.2.7_mac_aarch64 + +chmod +x ./bin/vina_* +``` + +## 项目使用 + +### 快速开始 + +```bash +# 1. 克隆项目 +git clone +cd vinatools + +# 2. 初始化 pixi 环境 +pixi init +pixi add rdkit openbabel meeko + +# 3. 下载 AutoDock Vina 二进制文件 +mkdir -p bin +curl -L -o ./bin/vina_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_1.2.7_mac_aarch64 +curl -L -o ./bin/vina_split_1.2.7_mac_aarch64 https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v1.2.7/vina_split_1.2.7_mac_aarch64 +chmod +x ./bin/vina_* + +# 4. 激活环境并运行 +pixi shell +``` + +### 环境管理命令 + +```bash +# 查看已安装的包 +pixi list + +# 添加新包 +pixi add package_name + +# 移除包 +pixi remove package_name + +# 更新所有包 +pixi update + +# 导出环境配置 +pixi export --format conda-lock +``` + +### 项目结构 + +``` +vinatools/ +├── bin/ # AutoDock Vina 二进制文件 +│ ├── vina_1.2.7_mac_aarch64 +│ └── vina_split_1.2.7_mac_aarch64 +├── scripts/ # Python 脚本 +│ ├── batch_prepare_ligands.sh +│ ├── batch_docking.sh +│ ├── calculate_qed_values.py +│ └── analyze_results.py +├── pixi.toml # pixi 环境配置文件 +├── docker/ # Docker 配置文件 +│ ├── Dockerfile +│ ├── docker-compose.yml +│ └── README.md +└── README.md +``` + +## Docker 环境 + +### 快速使用 Docker + +```bash +# 构建并运行 Docker 容器 +docker-compose -f docker/docker-compose.yml up -d + +# 进入容器 +docker-compose -f docker/docker-compose.yml exec vinatools bash + +# 运行脚本 +docker-compose -f docker/docker-compose.yml exec vinatools pixi run python scripts/calculate_qed_values.py +``` + +### Docker 服务说明 + +- **vinatools**: 主服务，包含 pixi 环境和所有依赖包 +- **jupyter**: Jupyter Notebook 服务，访问 http://localhost:8888 + +详细使用说明请参考 [docker/README.md](docker/README.md) \ No newline at end of file diff --git a/config/example.txt b/config/example.txt new file mode 100644 index 0000000..291b3f9 --- /dev/null +++ b/config/example.txt @@ -0,0 +1,6 @@ +center_x = -12.7 +center_y = -9.1 +center_z = -0.3 +size_x = 49.1 +size_y = 37.6 +size_z = 35.2 \ No newline at end of file diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 0000000..51638c3 --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,69 @@ +# 使用 Ubuntu 22.04 作为基础镜像（使用腾讯云镜像源） +FROM ccr.ccs.tencentyun.com/library/ubuntu:22.04 + +# 设置环境变量 +ENV DEBIAN_FRONTEND=noninteractive +ENV PATH="/root/.pixi/bin:$PATH" + +# 配置 APT 镜像源（使用阿里云镜像） +RUN sed -i 's@//.*archive.ubuntu.com@//mirrors.aliyun.com@g' /etc/apt/sources.list && \ + sed -i 's@//.*security.ubuntu.com@//mirrors.aliyun.com@g' /etc/apt/sources.list + +# 安装系统依赖 +RUN apt-get update && apt-get install -y \ + curl \ + wget \ + git \ + build-essential \ + ca-certificates \ + gnupg \ + lsb-release \ + && rm -rf /var/lib/apt/lists/* + +# 配置 pip 镜像源 +RUN mkdir -p /root/.pip && \ + echo "[global]" > /root/.pip/pip.conf && \ + echo "index-url = https://pypi.tuna.tsinghua.edu.cn/simple" >> /root/.pip/pip.conf && \ + echo "trusted-host = pypi.tuna.tsinghua.edu.cn" >> /root/.pip/pip.conf + +# 安装 pixi +RUN curl -fsSL https://pixi.sh/install.sh | bash + +# 设置工作目录 +WORKDIR /app + +# 复制项目文件 +COPY . . + +# 创建 bin 目录 +RUN mkdir -p bin + +# 下载 AutoDock Vina 二进制文件 +ARG VINA_VERSION=1.2.7 +ARG VINA_PLATFORM=mac_aarch64 +ARG DOWNLOAD_VINA=true + +RUN if [ "$DOWNLOAD_VINA" = "true" ]; then \ + curl -L -o ./bin/vina_${VINA_VERSION}_${VINA_PLATFORM} https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v${VINA_VERSION}/vina_${VINA_VERSION}_${VINA_PLATFORM} && \ + curl -L -o ./bin/vina_split_${VINA_VERSION}_${VINA_PLATFORM} https://github.com/ccsb-scripps/AutoDock-Vina/releases/download/v${VINA_VERSION}/vina_split_${VINA_VERSION}_${VINA_PLATFORM} && \ + chmod +x ./bin/vina_*; \ + fi + +# 添加平台支持并安装 pixi 包 +RUN /root/.pixi/bin/pixi workspace platform add linux-aarch64 && \ + /root/.pixi/bin/pixi add rdkit openbabel meeko + +# 设置环境变量 +ENV PATH="/root/.pixi/bin:/app/bin:$PATH" + +# 创建启动脚本 +RUN echo '#!/bin/bash\n\ +source /root/.bashrc\n\ +exec "$@"' > /entrypoint.sh && \ + chmod +x /entrypoint.sh + +# 设置入口点 +ENTRYPOINT ["/entrypoint.sh"] + +# 默认命令 +CMD ["/root/.pixi/bin/pixi", "shell"] diff --git a/docker/README.md b/docker/README.md new file mode 100644 index 0000000..f653e4c --- /dev/null +++ b/docker/README.md @@ -0,0 +1,201 @@ +# Docker 环境使用说明 + +## 快速开始 + +### 1. 环境变量配置 + +```bash +# 复制环境变量模板 +cp docker/docker.env.example docker/.env + +# 编辑环境变量 +vim docker/.env +``` + +### 2. 构建镜像 + +```bash +# 使用默认配置构建 +docker-compose -f docker/docker-compose.yml build + +# 使用环境变量构建 +docker-compose -f docker/docker-compose.yml --env-file docker/.env build + +# 或者直接使用 docker build +docker build -f docker/Dockerfile -t vinatools:latest . +``` + +### 3. 环境变量说明 + +| 变量名 | 默认值 | 说明 | +|--------|--------|------| +| `VINA_VERSION` | `1.2.7` | AutoDock Vina 版本 | +| `VINA_PLATFORM` | `mac_aarch64` | 平台架构 | +| `DOWNLOAD_VINA` | `true` | 是否下载 AutoDock Vina | + +**支持的平台：** +- `mac_aarch64` - Apple Silicon Mac +- `mac_x86_64` - Intel Mac +- `linux_x86_64` - Linux x86_64 +- `windows_x86_64` - Windows x86_64 + +### 4. 运行容器 + +```bash +# 启动主服务 +docker-compose -f docker/docker-compose.yml up -d vinatools + +# 进入容器 +docker-compose -f docker/docker-compose.yml exec vinatools bash + +# 或者直接运行 +docker run -it --rm -v $(pwd):/app vinatools:latest bash +``` + +## 使用示例 + +### 不同平台构建 + +```bash +# Linux x86_64 平台 +VINA_PLATFORM=linux_x86_64 docker-compose -f docker/docker-compose.yml build +VINA_PLATFORM=linux_aarch64 docker-compose -f docker/docker-compose.yml build + +# Intel Mac 平台 +VINA_PLATFORM=mac_x86_64 docker-compose -f docker/docker-compose.yml build + +# 不下载 AutoDock Vina +DOWNLOAD_VINA=false docker-compose -f docker/docker-compose.yml build +``` + +### 使用环境文件 + +```bash +# 创建自定义环境文件 +cat > docker/my.env << EOF +VINA_VERSION=1.2.6 +VINA_PLATFORM=linux_x86_64 +DOWNLOAD_VINA=true +EOF + +# 使用自定义环境文件构建 +docker-compose -f docker/docker-compose.yml --env-file docker/my.env build +``` + +### 3. 使用 Jupyter Notebook + +```bash +# 启动 Jupyter 服务 +docker-compose -f docker/docker-compose.yml up -d jupyter + +# 访问 http://localhost:8888 +``` + +## 环境说明 + +### 镜像源配置 +为了加速构建过程，Dockerfile 中配置了以下镜像源： + +- **APT 源**: 阿里云镜像 (mirrors.aliyun.com) +- **pip 源**: 清华大学镜像 (pypi.tuna.tsinghua.edu.cn) +- **conda 源**: 清华大学镜像 (mirrors.tuna.tsinghua.edu.cn) + +### 包含的包 +- **rdkit**: 化学信息学工具包 +- **openbabel**: 分子格式转换工具 +- **meeko**: 分子准备工具 +- **AutoDock Vina**: 分子对接工具 + +### 目录结构 +``` +/app/ +├── bin/ # AutoDock Vina 二进制文件 +├── scripts/ # Python 脚本 +├── data/ # 输入数据（挂载） +└── results/ # 输出结果（挂载） +``` + +## 常用命令 + +### 运行脚本 +```bash +# 在容器中运行 Python 脚本 +pixi run python scripts/calculate_qed_values.py + +# 运行批处理脚本 +pixi run bash scripts/batch_docking.sh +``` + +### 数据管理 +```bash +# 挂载数据目录 +docker run -it --rm \ + -v $(pwd)/data:/app/data \ + -v $(pwd)/results:/app/results \ + vinatools:latest bash +``` + +### 清理 +```bash +# 停止所有服务 +docker-compose -f docker/docker-compose.yml down + +# 删除镜像 +docker rmi vinatools:latest + +# 清理未使用的资源 +docker system prune -a +``` + +## 故障排除 + +### 网络连接问题 +如果遇到网络连接问题，可以尝试以下解决方案： + +```bash +# 1. 使用代理构建 +docker build --build-arg HTTP_PROXY=http://proxy:port \ + --build-arg HTTPS_PROXY=http://proxy:port \ + -f docker/Dockerfile -t vinatools:latest . + +# 2. 使用不同的镜像源 +# 编辑 Dockerfile，替换镜像源： +# - APT: mirrors.ustc.edu.cn (中科大) +# - pip: pypi.douban.com (豆瓣) +# - conda: mirrors.ustc.edu.cn/anaconda/cloud/ + +# 3. 离线构建（如果网络完全不可用） +# 预先下载所有依赖包，然后使用本地构建 +``` + +### 权限问题 +```bash +# 修复文件权限 +sudo chown -R $USER:$USER data/ results/ +``` + +### 内存不足 +```bash +# 增加 Docker 内存限制 +docker run -it --rm --memory=8g vinatools:latest bash +``` + +### 网络问题 +```bash +# 使用主机网络 +docker run -it --rm --network=host vinatools:latest bash +``` + +### 镜像源切换 +如果需要使用其他镜像源，可以修改 Dockerfile 中的配置： + +```dockerfile +# APT 源切换 +RUN sed -i 's@//.*archive.ubuntu.com@//mirrors.ustc.edu.cn@g' /etc/apt/sources.list + +# pip 源切换 +RUN echo "index-url = https://pypi.douban.com/simple" > /root/.pip/pip.conf + +# conda 源切换 +RUN /root/.pixi/bin/pixi config set channels https://mirrors.ustc.edu.cn/anaconda/cloud/conda-forge/ +``` diff --git a/docker/docker-compose.yml b/docker/docker-compose.yml new file mode 100644 index 0000000..118385a --- /dev/null +++ b/docker/docker-compose.yml @@ -0,0 +1,70 @@ +version: '3.8' + +services: + vinatools: + build: + context: .. + dockerfile: docker/Dockerfile + args: + VINA_VERSION: ${VINA_VERSION:-1.2.7} + VINA_PLATFORM: ${VINA_PLATFORM:-linux} + DOWNLOAD_VINA: ${DOWNLOAD_VINA:-true} + image: vinatools:latest + container_name: vinatools-container + volumes: + # 挂载项目目录到容器 + - ..:/app + # 挂载数据目录（用于输入输出文件） + - ./data:/app/data + - ./results:/app/results + working_dir: /app + environment: + - PIXI_ROOT=/root/.pixi + - PATH=/root/.pixi/bin:/app/bin:$PATH + # 保持容器运行 + tty: true + stdin_open: true + # 网络模式 + network_mode: host + # 重启策略 + restart: unless-stopped + # 资源限制 + deploy: + resources: + limits: + memory: 4G + cpus: '2.0' + reservations: + memory: 2G + cpus: '1.0' + + # 可选：用于 Jupyter Notebook 服务 + jupyter: + build: + context: .. + dockerfile: docker/Dockerfile + args: + VINA_VERSION: ${VINA_VERSION:-1.2.7} + VINA_PLATFORM: ${VINA_PLATFORM:-linux_x86_64} + DOWNLOAD_VINA: ${DOWNLOAD_VINA:-true} + image: vinatools:latest + container_name: vinatools-jupyter + ports: + - "8888:8888" + volumes: + - ..:/app + - ./data:/app/data + - ./results:/app/results + working_dir: /app + environment: + - PIXI_ROOT=/root/.pixi + - PATH=/root/.pixi/bin:/app/bin:$PATH + command: > + bash -c " + /root/.pixi/bin/pixi workspace platform add linux-aarch64 && + /root/.pixi/bin/pixi add jupyter notebook && + /root/.pixi/bin/pixi run jupyter notebook --ip=0.0.0.0 --port=8888 --no-browser --allow-root --NotebookApp.token='' --NotebookApp.password='' + " + restart: unless-stopped + depends_on: + - vinatools diff --git a/docker/docker.env.example b/docker/docker.env.example new file mode 100644 index 0000000..c034189 --- /dev/null +++ b/docker/docker.env.example @@ -0,0 +1,12 @@ +# AutoDock Vina 配置 +VINA_VERSION=1.2.7 +VINA_PLATFORM=mac_aarch64 +DOWNLOAD_VINA=true + +# 其他平台选项： +# VINA_PLATFORM=linux_x86_64 +# VINA_PLATFORM=mac_x86_64 +# VINA_PLATFORM=windows_x86_64 + +# 禁用 AutoDock Vina 下载 +# DOWNLOAD_VINA=false diff --git a/pixi.lock b/pixi.lock new file mode 100644 index 0000000..394bc17 --- /dev/null +++ b/pixi.lock @@ -0,0 +1,4345 @@ +version: 6 +environments: + default: + channels: + - url: https://conda.anaconda.org/conda-forge/ + packages: + linux-aarch64: + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/_openmp_mutex-4.5-2_gnu.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.11.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/argon2-cffi-bindings-25.1.0-py312hcd1a082_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.3.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.0-pyhd8ed1ab_1.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/async-lru-2.0.5-pyh29332c3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/attrs-25.4.0-pyh71513ae_0.conda + - 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"linux-aarch64"] +version = "0.1.0" + +[tasks] + +[dependencies] +rdkit = ">=2025.9.1,<2026" +openbabel = ">=3.1.1,<4" +meeko = ">=0.5.0,<0.6" +jupyter = ">=1.1.1,<2" +notebook = ">=7.4.7,<8" diff --git a/scripts/analyze_qed_mw_distribution.py b/scripts/analyze_qed_mw_distribution.py new file mode 100644 index 0000000..1916f04 --- /dev/null +++ b/scripts/analyze_qed_mw_distribution.py @@ -0,0 +1,551 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- +''' +@file :analyze_qed_mw_distribution.py +@Description :Analysis of QED and molecular weight distribution with KDE plots +@Date :2025/08/05 +@Author :lyzeng +''' + +import pandas as pd +import seaborn as sns +import matplotlib.pyplot as plt +import numpy as np +from pathlib import Path +from rdkit import Chem +import logging +import ast +import json +import click + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def load_dataset(csv_file): + """ + Load dataset from CSV file + + Args: + csv_file (str): Path to the CSV file + + Returns: + pd.DataFrame: Loaded dataset + """ + df = pd.read_csv(csv_file) + logger.info(f"Loaded {len(df)} records from {csv_file}") + + # Print basic statistics + logger.info(f"Statistics for {Path(csv_file).stem}:") + logger.info(f"QED - Min: {df['qed'].min():.3f}, Max: {df['qed'].max():.3f}, Mean: {df['qed'].mean():.3f}") + logger.info(f"Molecular Weight - Min: {df['molecular_weight'].min():.2f}, Max: {df['molecular_weight'].max():.2f}, Mean: {df['molecular_weight'].mean():.2f}") + + return df + +def load_reference_molecules(dataset_name): + """ + Load reference molecules from CSV file + + Args: + dataset_name (str): Name of the dataset (fgbar or trpe) + + Returns: + pd.DataFrame: Reference molecules with QED and molecular weight + """ + # Load reference molecules from the main CSV file + csv_files = list(Path(".").glob(f"qed_values_{dataset_name}.csv")) + if not csv_files: + logger.warning(f"No CSV file found for {dataset_name}") + return pd.DataFrame() + + df = pd.read_csv(csv_files[0]) + + # Filter for reference molecules (those that have align_ and _out_converted.sdf in their filename) + reference_df = df[df['filename'].str.contains('align_.*_out_converted\.sdf', na=False, regex=True)] + + logger.info(f"Loaded {len(reference_df)} reference molecules for {dataset_name}") + return reference_df + +def extract_vina_scores_from_sdf(sdf_file_path): + """ + Extract Vina scores from all conformers in an SDF file + + Args: + sdf_file_path (str): Path to the SDF file + + Returns: + list: List of Vina scores (free_energy values) or empty list if failed + """ + scores = [] + try: + supplier = Chem.SDMolSupplier(sdf_file_path, removeHs=False) + for mol in supplier: + if mol is None: + continue + + # Get the meeko property which contains docking information + if mol.HasProp("meeko"): + meeko_raw = mol.GetProp("meeko") + try: + meeko_dict = json.loads(meeko_raw) + # Extract free energy (Vina score) + if 'free_energy' in meeko_dict: + scores.append(meeko_dict['free_energy']) + except json.JSONDecodeError: + logger.warning(f"Failed to parse meeko JSON for {sdf_file_path}") + else: + logger.warning(f"No meeko property found in molecule from {sdf_file_path}") + except Exception as e: + logger.warning(f"Failed to extract Vina scores from {sdf_file_path}: {e}") + + return scores + +def load_vina_scores_from_csv(df, max_files=1000): + """ + Load Vina scores from the CSV file + + Args: + df (pd.DataFrame): DataFrame with vina_scores column + max_files (int): Maximum number of files to process + + Returns: + list: List of all Vina scores from all molecules + """ + all_vina_scores = [] + + # Process only up to max_files to avoid memory issues + processed_files = 0 + + for idx, row in df.iterrows(): + if processed_files >= max_files: + break + + # Skip reference molecules (those with mol2 extension) + if '.mol2' in row['filename']: + continue + + try: + # Parse the vina_scores string back to a list + vina_scores = ast.literal_eval(row['vina_scores']) + all_vina_scores.extend(vina_scores) + processed_files += 1 + except (ValueError, SyntaxError) as e: + logger.warning(f"Failed to parse Vina scores for {row['filename']}: {e}") + + logger.info(f"Loaded {len(all_vina_scores)} Vina scores from {processed_files} files") + return all_vina_scores + +def get_min_vina_scores_length(df): + """ + Get the minimum length of vina_scores lists in the dataframe + + Args: + df (pd.DataFrame): DataFrame with vina_scores column + + Returns: + int: Minimum length of vina_scores lists + """ + min_length = float('inf') + + for idx, row in df.iterrows(): + # Skip reference molecules (those with mol2 extension) + if '.mol2' in row['filename']: + continue + + try: + # Parse the vina_scores string back to a list + vina_scores = ast.literal_eval(row['vina_scores']) + min_length = min(min_length, len(vina_scores)) + except (ValueError, SyntaxError) as e: + logger.warning(f"Failed to parse Vina scores for {row['filename']}: {e}") + + return min_length if min_length != float('inf') else 0 + +def get_reference_vina_scores(dataset_name, rank=0): + """ + Get Vina scores for reference molecules + + Args: + dataset_name (str): Name of the dataset (fgbar or trpe) + rank (int): Rank of the conformation to use (0 for best/first) + + Returns: + dict: Dictionary with reference molecule identifiers and their Vina scores + """ + reference_scores = {} + + # 使用原始目录名称 "refence" + reference_dir = Path("result") / "refence" / dataset_name + + if not reference_dir.exists(): + logger.warning(f"Reference directory {reference_dir} does not exist") + return reference_scores + + # Find reference SDF files + reference_sdf_files = list(reference_dir.glob("*_converted.sdf")) + logger.info(f"Processing {len(reference_sdf_files)} reference SDF files in {reference_dir}") + + for sdf_file in reference_sdf_files: + vina_scores = extract_vina_scores_from_sdf(str(sdf_file)) + if vina_scores: + # Check if rank is valid + if rank >= len(vina_scores): + raise ValueError(f"Rank {rank} is out of range. The minimum number of conformers across all molecules is {len(vina_scores)}. Please choose a rank less than {len(vina_scores)}.") + + # Get the score at the specified rank + reference_score = vina_scores[rank] + + # Extract identifier from filename + filename_stem = sdf_file.stem + if '_out_converted' in filename_stem: + filename_stem = filename_stem.replace('_out_converted', '') + if '_addH' in filename_stem: + filename_stem = filename_stem.replace('_addH', '') + if 'align_' in filename_stem: + filename_stem = filename_stem.split('_')[-1] # Get the last part (e.g., 9NY or 0GA) + + # Use filename_stem as key for reference_scores + reference_scores[filename_stem] = reference_score + logger.info(f"Reference Vina score for {filename_stem} (rank {rank}): {reference_score}") + + return reference_scores + +def plot_combined_kde_distribution_normalized(df, dataset_name, reference_df=None, reference_scores=None, vina_scores=None, reference_vina_scores=None): + """ + Plot combined KDE distribution for QED, molecular weight, and Vina scores (normalized) + + Args: + df (pd.DataFrame): Main dataset + dataset_name (str): Name of the dataset (fgbar or trpe) + reference_df (pd.DataFrame): Reference molecules dataset (optional) + reference_scores (dict): Reference molecule scores (optional) + vina_scores (list): Vina scores for all molecules (optional) + reference_vina_scores (dict): Reference molecule Vina scores (optional) + """ + # Create figure + plt.figure(figsize=(15, 8)) + + # Normalize the data to make them comparable on the same scale + qed_normalized = (df['qed'] - df['qed'].min()) / (df['qed'].max() - df['qed'].min()) + mw_normalized = (df['molecular_weight'] - df['molecular_weight'].min()) / (df['molecular_weight'].max() - df['molecular_weight'].min()) + + # Plot KDE for normalized QED + sns.kdeplot(qed_normalized, label='QED (normalized)', fill=True, alpha=0.5, color='blue') + + # Plot KDE for normalized molecular weight + sns.kdeplot(mw_normalized, label='Molecular Weight (normalized)', fill=True, alpha=0.5, color='red') + + # Plot KDE for normalized Vina scores if available + if vina_scores and len(vina_scores) > 0: + # Normalize Vina scores (note: lower scores are better, so we negate for visualization) + vina_series = pd.Series(vina_scores) + vina_normalized = (vina_series - vina_series.min()) / (vina_series.max() - vina_series.min()) + sns.kdeplot(vina_normalized, label='Vina Score (normalized)', fill=True, alpha=0.5, color='green') + + # Mark reference molecules if provided + if reference_df is not None and len(reference_df) > 0: + # Normalize reference data using the same scale as main dataset + ref_qed_normalized = (reference_df['qed'] - df['qed'].min()) / (df['qed'].max() - df['qed'].min()) + ref_mw_normalized = (reference_df['molecular_weight'] - df['molecular_weight'].min()) / (df['molecular_weight'].max() - df['molecular_weight'].min()) + + # Dictionary to store reference positions for legend + legend_handles = [] + + # Mark reference molecules for QED, MW, and Vina scores + for i, (idx, row) in enumerate(reference_df.iterrows()): + filename_stem = Path(row['filename']).stem + # Extract actual identifier from filename + if '_addH' in filename_stem: + filename_stem = filename_stem.replace('_addH', '') + if 'align_' in filename_stem: + filename_stem = filename_stem.split('_')[-1] # Get the last part (e.g., 9NY or 0GA) + + # Get values from the reference dataframe + qed_value = row['qed'] + mw_value = row['molecular_weight'] + + # Build score text + score_text = f"{filename_stem}\n(QED: {qed_value:.2f}, MW: {mw_value:.2f}" + + # Add Vina score if available + if reference_vina_scores and filename_stem in reference_vina_scores: + vina_score = reference_vina_scores[filename_stem] + score_text += f", Vina: {vina_score:.2f}" + score_text += ")" + + # QED marker + x_pos = ref_qed_normalized.iloc[i] + plt.scatter(x_pos, 0, color='darkblue', s=100, marker='v', zorder=5) + + # Molecular weight marker + x_pos = ref_mw_normalized.iloc[i] + plt.scatter(x_pos, 0, color='darkred', s=100, marker='^', zorder=5) + + # Vina score marker if available + if reference_vina_scores and filename_stem in reference_vina_scores: + # Normalize reference Vina score + vina_min = min(vina_scores) + vina_max = max(vina_scores) + ref_vina_normalized = (reference_vina_scores[filename_stem] - vina_min) / (vina_max - vina_min) + plt.scatter(ref_vina_normalized, 0, color='darkgreen', s=100, marker='o', zorder=5) + + # Annotate with combined information + plt.annotate(score_text, + (x_pos, 0), + xytext=(10, 30), + textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'), + fontsize=8) + + # Add to legend + legend_handles.append(plt.Line2D([0], [0], marker='v', color='darkblue', label=f"{filename_stem} - QED", + markerfacecolor='darkblue', markersize=8, linestyle='')) + legend_handles.append(plt.Line2D([0], [0], marker='^', color='darkred', label=f"{filename_stem} - MW", + markerfacecolor='darkred', markersize=8, linestyle='')) + if reference_vina_scores and filename_stem in reference_vina_scores: + legend_handles.append(plt.Line2D([0], [0], marker='o', color='darkgreen', label=f"{filename_stem} - Vina", + markerfacecolor='darkgreen', markersize=8, linestyle='')) + + # Add combined legend + plt.legend(handles=legend_handles, loc='upper right', fontsize=10) + + plt.title(f'Combined KDE Distribution (Normalized) - {dataset_name.upper()}', fontsize=16) + plt.xlabel('Normalized Values (0-1)') + plt.ylabel('Density') + plt.legend() + plt.grid(True, alpha=0.3) + + # Adjust layout and save figure + plt.tight_layout() + plt.savefig(f'kde_distribution_{dataset_name}_normalized.png', dpi=300, bbox_inches='tight') + plt.close() + logger.info(f"Saved combined KDE distribution plot (normalized) for {dataset_name} as kde_distribution_{dataset_name}_normalized.png") + +def plot_combined_kde_distribution_actual(df, dataset_name, reference_df=None, reference_scores=None, vina_scores=None, reference_vina_scores=None): + """ + Plot combined KDE distribution for QED, molecular weight, and Vina scores (actual values) + + Args: + df (pd.DataFrame): Main dataset + dataset_name (str): Name of the dataset (fgbar or trpe) + reference_df (pd.DataFrame): Reference molecules dataset (optional) + reference_scores (dict): Reference molecule scores (optional) + vina_scores (list): Vina scores for all molecules (optional) + reference_vina_scores (dict): Reference molecule Vina scores (optional) + """ + # Create figure with subplots + fig, axes = plt.subplots(1, 3, figsize=(18, 6)) + fig.suptitle(f'KDE Distribution (Actual Values) - {dataset_name.upper()}', fontsize=16) + + # Get reference molecule identifier (stem of the SDF filename) + reference_filename_stem = None + if reference_df is not None and len(reference_df) > 0: + reference_filename_stem = Path(reference_df.iloc[0]['filename']).stem + if '_out_converted' in reference_filename_stem: + reference_filename_stem = reference_filename_stem.replace('_out_converted', '') + if '_addH' in reference_filename_stem: + reference_filename_stem = reference_filename_stem.replace('_addH', '') + if 'align_' in reference_filename_stem: + reference_filename_stem = reference_filename_stem.split('_')[-1] # Get the last part (e.g., 9NY or 0GA) + + # Plot 1: QED distribution + sns.kdeplot(df['qed'], ax=axes[0], fill=True, alpha=0.5, color='blue') + axes[0].set_title('QED Distribution') + axes[0].set_xlabel('QED Value') + axes[0].set_ylabel('Density') + axes[0].grid(True, alpha=0.3) + + # Mark reference molecules for QED + if reference_df is not None and len(reference_df) > 0: + for i, (idx, row) in enumerate(reference_df.iterrows()): + filename_stem = Path(row['filename']).stem + # Extract actual identifier from filename + if '_addH' in filename_stem: + filename_stem = filename_stem.replace('_addH', '') + if 'align_' in filename_stem: + filename_stem = filename_stem.split('_')[-1] # Get the last part (e.g., 9NY or 0GA) + + # Get QED value from the reference dataframe + qed_value = row['qed'] + score_text = f"{reference_filename_stem}\n({qed_value:.2f})" + + axes[0].scatter(row['qed'], 0, color='darkblue', s=100, marker='v', zorder=5) + axes[0].annotate(score_text, + (row['qed'], 0), + xytext=(10, 20), + textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'), + fontsize=8) + + # Plot 2: Molecular weight distribution + sns.kdeplot(df['molecular_weight'], ax=axes[1], fill=True, alpha=0.5, color='red') + axes[1].set_title('Molecular Weight Distribution') + axes[1].set_xlabel('Molecular Weight (Daltons)') + axes[1].set_ylabel('Density') + axes[1].grid(True, alpha=0.3) + + # Mark reference molecules for molecular weight + if reference_df is not None and len(reference_df) > 0: + for i, (idx, row) in enumerate(reference_df.iterrows()): + filename_stem = Path(row['filename']).stem + # Extract actual identifier from filename + if '_addH' in filename_stem: + filename_stem = filename_stem.replace('_addH', '') + if 'align_' in filename_stem: + filename_stem = filename_stem.split('_')[-1] # Get the last part (e.g., 9NY or 0GA) + + # Get MW value from the reference dataframe + mw_value = row['molecular_weight'] + score_text = f"{reference_filename_stem}\n({mw_value:.2f})" + + axes[1].scatter(row['molecular_weight'], 0, color='darkred', s=100, marker='^', zorder=5) + axes[1].annotate(score_text, + (row['molecular_weight'], 0), + xytext=(10, -30), + textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'), + fontsize=8) + + # Plot 3: Vina scores distribution + if vina_scores and len(vina_scores) > 0: + vina_series = pd.Series(vina_scores) + sns.kdeplot(vina_series, ax=axes[2], fill=True, alpha=0.5, color='green') + axes[2].set_title('Vina Score Distribution') + axes[2].set_xlabel('Vina Score (kcal/mol)') + axes[2].set_ylabel('Density') + axes[2].grid(True, alpha=0.3) + + # Mark reference molecules for Vina scores + if reference_vina_scores: + for filename_stem, vina_score in reference_vina_scores.items(): + score_text = f"{reference_filename_stem}\n({vina_score:.2f})" + + axes[2].scatter(vina_score, 0, color='darkgreen', s=100, marker='o', zorder=5) + axes[2].annotate(score_text, + (vina_score, 0), + xytext=(10, -60), + textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='white', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0'), + fontsize=8) + + # Adjust layout and save figure + plt.tight_layout() + plt.savefig(f'kde_distribution_{dataset_name}_actual.png', dpi=300, bbox_inches='tight') + plt.close() + logger.info(f"Saved combined KDE distribution plot (actual values) for {dataset_name} as kde_distribution_{dataset_name}_actual.png") + +def analyze_dataset(csv_file, dataset_name, reference_scores=None, rank=0): + """ + Analyze a dataset and generate KDE plots + + Args: + csv_file (str): Path to the CSV file + dataset_name (str): Name of the dataset (fgbar or trpe) + reference_scores (dict): Reference scores for each dataset + rank (int): Rank of the conformation to use for reference Vina scores (0 for best/first) + """ + # Load main dataset + df = load_dataset(csv_file) + + # Check minimum vina scores length + min_vina_length = get_min_vina_scores_length(df) + if rank >= min_vina_length: + raise ValueError(f"Rank {rank} is out of range. The minimum number of conformers across all molecules is {min_vina_length}. Please choose a rank less than {min_vina_length}.") + + # Load reference molecules + reference_df = load_reference_molecules(dataset_name) + + # Load Vina scores from CSV + vina_scores = load_vina_scores_from_csv(df) + + # Get reference Vina scores + reference_vina_scores = get_reference_vina_scores(dataset_name, rank) + + # Plot combined KDE distributions (normalized) + plot_combined_kde_distribution_normalized(df, dataset_name, reference_df, reference_scores, vina_scores, reference_vina_scores) + + # Plot combined KDE distributions (actual values) + plot_combined_kde_distribution_actual(df, dataset_name, reference_df, reference_scores, vina_scores, reference_vina_scores) + +def get_default_reference_scores(): + """ + Get default reference scores from README.md + + Returns: + dict: Dictionary with default reference scores + """ + # Default reference scores from README.md + return { + 'fgbar': { + '9NY': -5.268 # From README.md + }, + 'trpe': { + '0GA': -6.531 # From README.md + } + } + +@click.command() +@click.argument('csv_files', type=click.Path(exists=True), nargs=-1) +@click.option('--dataset-names', '-d', multiple=True, help='Names of the datasets corresponding to CSV files') +@click.option('--reference-scores', '-r', type=str, help='Reference scores in JSON format') +@click.option('--rank', '-k', default=0, type=int, help='Rank of conformation to use for reference Vina scores (default: 0 for best/first)') +def main_cli(csv_files, dataset_names, reference_scores, rank): + """ + Analyze QED and molecular weight distributions and generate KDE plots + + CSV_FILES: Paths to the CSV files with QED and molecular weight data + """ + if not csv_files: + logger.error("At least one CSV file must be provided") + return + + # Convert dataset names to list + dataset_names_list = list(dataset_names) if dataset_names else None + + # Parse reference scores if provided + if reference_scores: + try: + reference_scores_dict = json.loads(reference_scores) + except json.JSONDecodeError: + logger.error("Invalid JSON format for reference scores") + return + else: + reference_scores_dict = get_default_reference_scores() + + # Run main analysis + try: + main_api(csv_files, dataset_names_list, reference_scores_dict, rank) + except Exception as e: + logger.error(f"Analysis failed: {e}") + raise + +def main_api(csv_files, dataset_names=None, reference_scores=None, rank=0): + """ + Main function for API usage + + Args: + csv_files (list): List of CSV files to analyze + dataset_names (list): List of dataset names corresponding to CSV files + reference_scores (dict): Reference scores for each dataset + rank (int): Rank of the conformation to use for reference Vina scores (0 for best/first) + """ + if dataset_names is None: + dataset_names = [Path(f).stem.replace('qed_values_', '') for f in csv_files] + + if reference_scores is None: + reference_scores = get_default_reference_scores() + + for csv_file, dataset_name in zip(csv_files, dataset_names): + try: + logger.info(f"Analyzing dataset: {dataset_name}") + analyze_dataset(csv_file, dataset_name, reference_scores.get(dataset_name, {}), rank) + except Exception as e: + logger.error(f"Error analyzing {dataset_name}: {e}") + raise + +if __name__ == "__main__": + main_cli() \ No newline at end of file diff --git a/scripts/analyze_results.py b/scripts/analyze_results.py new file mode 100644 index 0000000..6715bd3 --- /dev/null +++ b/scripts/analyze_results.py @@ -0,0 +1,151 @@ +#!/usr/bin/env python3 +""" +分析AutoDock Vina对接结果脚本 +用法: python analyze_results.py poses_directory output_csv +""" + +import os +import sys +import pandas as pd +import re +from pathlib import Path + +def parse_vina_output(pdbqt_file): + """解析Vina输出文件，提取能量信息""" + results = [] + + try: + with open(pdbqt_file, 'r') as f: + lines = f.readlines() + + ligand_name = Path(pdbqt_file).stem.replace('_out', '') + model_num = 0 + + for line in lines: + if line.startswith('REMARK VINA RESULT:'): + # 解析能量信息 + # 格式: REMARK VINA RESULT: -8.5 0.000 0.000 + parts = line.strip().split() + if len(parts) >= 4: + binding_energy = float(parts[3]) + rmsd_lb = float(parts[4]) if len(parts) > 4 else 0.0 + rmsd_ub = float(parts[5]) if len(parts) > 5 else 0.0 + + model_num += 1 + results.append({ + 'ligand_name': ligand_name, + 'model': model_num, + 'binding_energy': binding_energy, + 'rmsd_lb': rmsd_lb, + 'rmsd_ub': rmsd_ub, + 'file_path': pdbqt_file + }) + + except Exception as e: + print(f"解析文件失败 {pdbqt_file}: {e}") + + return results + +def analyze_poses_directory(poses_dir): + """分析整个poses目录""" + all_results = [] + + poses_path = Path(poses_dir) + if not poses_path.exists(): + print(f"错误: 目录不存在 {poses_dir}") + return [] + + # 查找所有_out.pdbqt文件 + pdbqt_files = list(poses_path.glob("*_out.pdbqt")) + + print(f"找到 {len(pdbqt_files)} 个结果文件") + + for pdbqt_file in pdbqt_files: + results = parse_vina_output(str(pdbqt_file)) + all_results.extend(results) + + return all_results + +def generate_summary_stats(df): + """生成汇总统计信息""" + if df.empty: + return {} + + # 获取每个配体的最佳结合能 + best_poses = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()] + + stats = { + 'total_ligands': df['ligand_name'].nunique(), + 'total_poses': len(df), + 'best_binding_energy': df['binding_energy'].min(), + 'worst_binding_energy': df['binding_energy'].max(), + 'mean_binding_energy': df['binding_energy'].mean(), + 'median_binding_energy': df['binding_energy'].median(), + 'std_binding_energy': df['binding_energy'].std(), + 'ligands_better_than_minus_7': (best_poses['binding_energy'] < -7.0).sum(), + 'ligands_better_than_minus_8': (best_poses['binding_energy'] < -8.0).sum(), + 'ligands_better_than_minus_9': (best_poses['binding_energy'] < -9.0).sum(), + } + + return stats + +def main(): + if len(sys.argv) != 3: + print("用法: python analyze_results.py <输出CSV文件>") + sys.exit(1) + + poses_dir = sys.argv[1] + output_csv = sys.argv[2] + + print("开始分析对接结果...") + + # 分析所有结果 + results = analyze_poses_directory(poses_dir) + + if not results: + print("没有找到有效的结果文件") + sys.exit(1) + + # 转换为DataFrame + df = pd.DataFrame(results) + + # 保存详细结果 + df.to_csv(output_csv, index=False) + print(f"详细结果已保存到: {output_csv}") + + # 生成汇总统计 + stats = generate_summary_stats(df) + + # 打印汇总信息 + print("\n=== 对接结果汇总 ===") + print(f"总配体数量: {stats['total_ligands']}") + print(f"总构象数量: {stats['total_poses']}") + print(f"最佳结合能: {stats['best_binding_energy']:.2f} kcal/mol") + print(f"最差结合能: {stats['worst_binding_energy']:.2f} kcal/mol") + print(f"平均结合能: {stats['mean_binding_energy']:.2f} kcal/mol") + print(f"中位结合能: {stats['median_binding_energy']:.2f} kcal/mol") + print(f"标准差: {stats['std_binding_energy']:.2f} kcal/mol") + print(f"结合能 < -7.0 的配体: {stats['ligands_better_than_minus_7']}") + print(f"结合能 < -8.0 的配体: {stats['ligands_better_than_minus_8']}") + print(f"结合能 < -9.0 的配体: {stats['ligands_better_than_minus_9']}") + + # 显示前10个最佳结果 + best_results = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()] + best_results = best_results.sort_values('binding_energy').head(10) + + print("\n=== 前10个最佳结果 ===") + for _, row in best_results.iterrows(): + print(f"{row['ligand_name']}: {row['binding_energy']:.2f} kcal/mol") + + # 保存汇总统计 + summary_file = output_csv.replace('.csv', '_summary.txt') + with open(summary_file, 'w') as f: + f.write("AutoDock Vina 对接结果汇总\n") + f.write("=" * 30 + "\n\n") + for key, value in stats.items(): + f.write(f"{key}: {value}\n") + + print(f"\n汇总统计已保存到: {summary_file}") + +if __name__ == "__main__": + main() diff --git a/scripts/calculate_qed_values.py b/scripts/calculate_qed_values.py new file mode 100644 index 0000000..8c39b5a --- /dev/null +++ b/scripts/calculate_qed_values.py @@ -0,0 +1,228 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- +''' +@file :calculate_qed_values.py +@Description :Calculate QED values for molecules in poses_all directories and reference molecules +@Date :2025/08/04 +@Author :lyzeng +''' + +import pandas as pd +from rdkit import Chem +from rdkit.Chem import QED +from rdkit.Chem.Descriptors import MolWt +from pathlib import Path +import logging +import json + +# Setup logging +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +def get_smiles_from_sdf(sdf_file_path): + """ + Extract SMILES from the first molecule in an SDF file + + Args: + sdf_file_path (Path): Path to the SDF file + + Returns: + str: SMILES representation of the first molecule or None if failed + """ + try: + supplier = Chem.SDMolSupplier(str(sdf_file_path)) + mol = next(supplier) # Get the first molecule + if mol is not None: + smiles = Chem.MolToSmiles(mol) + return smiles + except Exception as e: + logger.warning(f"Failed to extract SMILES from {sdf_file_path}: {e}") + return None + +def get_smiles_from_mol2(mol2_file_path): + """ + Extract SMILES from a mol2 file + + Args: + mol2_file_path (Path): Path to the mol2 file + + Returns: + str: SMILES representation of the molecule or None if failed + """ + try: + mol = Chem.MolFromMol2File(str(mol2_file_path)) + if mol is not None: + smiles = Chem.MolToSmiles(mol) + return smiles + except Exception as e: + logger.warning(f"Failed to extract SMILES from {mol2_file_path}: {e}") + return None + +def extract_vina_scores_from_sdf(sdf_file_path): + """ + Extract Vina scores from all conformers in an SDF file + + Args: + sdf_file_path (Path): Path to the SDF file + + Returns: + list: List of Vina scores (free_energy values) or empty list if failed + """ + scores = [] + try: + supplier = Chem.SDMolSupplier(str(sdf_file_path), removeHs=False) + for mol in supplier: + if mol is None: + continue + + # Get the meeko property which contains docking information + if mol.HasProp("meeko"): + meeko_raw = mol.GetProp("meeko") + try: + meeko_dict = json.loads(meeko_raw) + # Extract free energy (Vina score) + if 'free_energy' in meeko_dict: + scores.append(meeko_dict['free_energy']) + except json.JSONDecodeError: + logger.warning(f"Failed to parse meeko JSON for {sdf_file_path}") + else: + logger.warning(f"No meeko property found in molecule from {sdf_file_path}") + except Exception as e: + logger.warning(f"Failed to extract Vina scores from {sdf_file_path}: {e}") + + return scores + +def calculate_qed_for_poses_all(base_dir, dataset_name): + """ + Calculate QED values for all SDF files in poses_all directory + + Args: + base_dir (Path): Base directory containing the poses_all folder + dataset_name (str): Name of the dataset (fgbar or trpe) + + Returns: + list: List of dictionaries with smiles, filename, qed, and molecular weight values + """ + results = [] + poses_all_dir = base_dir / dataset_name / "poses_all" + + if not poses_all_dir.exists(): + logger.warning(f"Directory {poses_all_dir} does not exist") + return results + + sdf_files = list(poses_all_dir.glob("*.sdf")) + logger.info(f"Processing {len(sdf_files)} SDF files in {poses_all_dir}") + + for sdf_file in sdf_files: + smiles = get_smiles_from_sdf(sdf_file) + vina_scores = extract_vina_scores_from_sdf(sdf_file) + + if smiles is not None: + try: + mol = Chem.MolFromSmiles(smiles) + if mol is not None: + qed_value = QED.qed(mol) + mol_weight = MolWt(mol) + results.append({ + 'smiles': smiles, + 'filename': sdf_file.name, + 'qed': qed_value, + 'molecular_weight': mol_weight, + 'vina_scores': vina_scores # 添加Vina得分列表 + }) + except Exception as e: + logger.warning(f"Failed to calculate QED for {sdf_file}: {e}") + + return results + +def calculate_qed_for_reference(base_dir, dataset_name): + """ + Calculate QED values for reference molecules in SDF format + + Args: + base_dir (Path): Base directory containing the reference folder + dataset_name (str): Name of the dataset (fgbar or trpe) + + Returns: + list: List of dictionaries with smiles, filename, qed, molecular weight, and vina scores values + """ + results = [] + # 使用原始目录名称 "refence" + reference_dir = base_dir / "refence" / dataset_name + + if not reference_dir.exists(): + logger.warning(f"Directory {reference_dir} does not exist") + return results + + # 查找参考分子的SDF文件 + reference_sdf_files = list(reference_dir.glob("*_out_converted.sdf")) + logger.info(f"Processing {len(reference_sdf_files)} reference SDF files in {reference_dir}") + + for sdf_file in reference_sdf_files: + smiles = get_smiles_from_sdf(sdf_file) + vina_scores = extract_vina_scores_from_sdf(sdf_file) + + if smiles is not None: + try: + mol = Chem.MolFromSmiles(smiles) + if mol is not None: + qed_value = QED.qed(mol) + mol_weight = MolWt(mol) + results.append({ + 'smiles': smiles, + 'filename': sdf_file.name, + 'qed': qed_value, + 'molecular_weight': mol_weight, + 'vina_scores': str(vina_scores) # 添加Vina得分列表 + }) + except Exception as e: + logger.warning(f"Failed to calculate QED for {sdf_file}: {e}") + + return results + +def process_dataset(result_dir, dataset_name): + """ + Process a single dataset (fgbar or trpe) and save to a separate CSV file + + Args: + result_dir (Path): Base result directory + dataset_name (str): Name of the dataset (fgbar or trpe) + """ + # Process poses_all SDF files + poses_results = calculate_qed_for_poses_all(result_dir, dataset_name) + + # Process reference SDF files + reference_results = calculate_qed_for_reference(result_dir, dataset_name) + + # Combine results + all_results = poses_results + reference_results + + # Create DataFrame and save to CSV + if all_results: + df = pd.DataFrame(all_results) + csv_filename = f"qed_values_{dataset_name}.csv" + df.to_csv(csv_filename, index=False) + logger.info(f"Saved {len(df)} QED values to {csv_filename}") + print(f"First few rows of {csv_filename}:") + print(df.head()) + else: + logger.warning(f"No QED values were calculated for {dataset_name}") + # Create empty CSV with headers + df = pd.DataFrame(columns=['smiles', 'filename', 'qed', 'molecular_weight', 'vina_scores']) + csv_filename = f"qed_values_{dataset_name}.csv" + df.to_csv(csv_filename, index=False) + +def main(): + """ + Main function to calculate QED values for all molecules + """ + # Define base directories + result_dir = Path("result") + + # Process both datasets (fgbar and trpe) separately + datasets = ["fgbar", "trpe"] + for dataset in datasets: + process_dataset(result_dir, dataset) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/example_api_usage.py b/scripts/example_api_usage.py new file mode 100644 index 0000000..f46b3ed --- /dev/null +++ b/scripts/example_api_usage.py @@ -0,0 +1,30 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +""" +Example usage of the analyze_qed_mw_distribution API +""" + +from analyze_qed_mw_distribution import main_api + +print("Running analysis examples...") + +# Example 1: Basic usage +print("\nExample 1: Basic usage") +main_api(['qed_values_fgbar.csv', 'qed_values_trpe.csv'], ['fgbar', 'trpe']) + +# Example 2: With custom reference scores +print("\nExample 2: With custom reference scores") +main_api(['qed_values_fgbar.csv', 'qed_values_trpe.csv'], ['fgbar', 'trpe'], + reference_scores={'fgbar': {'9NY': -5.268}, 'trpe': {'0GA': -6.531}}) + +# Example 3: With specific conformation rank +print("\nExample 3: With specific conformation rank") +main_api(['qed_values_fgbar.csv', 'qed_values_trpe.csv'], ['fgbar', 'trpe'], rank=0) + +# Example 4: With both custom reference scores and specific conformation rank +print("\nExample 4: With both custom reference scores and specific conformation rank") +main_api(['qed_values_fgbar.csv', 'qed_values_trpe.csv'], ['fgbar', 'trpe'], + reference_scores={'fgbar': {'9NY': -5.268}, 'trpe': {'0GA': -6.531}}, rank=0) + +print("\nAnalysis complete! Check the generated PNG files.") \ No newline at end of file diff --git a/scripts/qed.py b/scripts/qed.py new file mode 100644 index 0000000..1e7d06a --- /dev/null +++ b/scripts/qed.py @@ -0,0 +1,201 @@ +#!/usr/bin/env python +# -*- encoding: utf-8 -*- +''' +@file :qed_calculator.py +@Description :QED calculator with joblib parallel support +@Date :2025/08/04 +@Author :lyzeng +''' + +from rdkit import Chem +from rdkit.Chem import QED +import pandas as pd +from typing import List, Union, Tuple, Optional +import joblib +from joblib import Parallel, delayed +import logging + +# 设置日志 +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + + +def calculate_single_qed(smiles: str) -> Optional[Tuple[str, float]]: + """ + Calculate QED value for a single molecule. + + Args: + smiles (str): SMILES representation of the molecule + + Returns: + tuple: (smiles, qed_value) or None if calculation fails + """ + try: + mol = Chem.MolFromSmiles(smiles) + if mol is not None: + qed_value = QED.qed(mol) + return (smiles, qed_value) + except Exception as e: + logger.warning(f"Failed to calculate QED for {smiles}: {e}") + pass + return None + + +def parallel_qed_calculation( + smiles_list: List[str], + n_jobs: int = -1, + batch_size: Union[int, str] = "auto", + backend: str = "loky" +) -> pd.DataFrame: + """ + Calculate QED values for a list of SMILES in parallel using joblib. + + Args: + smiles_list (List[str]): List of SMILES strings + n_jobs (int): Number of parallel jobs. -1 means using all processors + batch_size (int or str): Batch size for parallel processing + backend (str): Joblib backend to use ('loky', 'threading', 'multiprocessing') + + Returns: + pd.DataFrame: DataFrame with 'smiles' and 'qed' columns + """ + logger.info(f"Calculating QED values for {len(smiles_list)} molecules...") + + # 并行计算QED值 + results = Parallel( + n_jobs=n_jobs, + batch_size=batch_size, + backend=backend + )(delayed(calculate_single_qed)(smiles) for smiles in smiles_list) + + # 过滤掉None结果 + valid_results = [r for r in results if r is not None] + + if not valid_results: + logger.warning("No valid QED values calculated") + return pd.DataFrame(columns=['smiles', 'qed']) + + # 分离SMILES和QED值 + smiles_values, qed_values = zip(*valid_results) + + # 创建DataFrame + df = pd.DataFrame({ + 'smiles': smiles_values, + 'qed': qed_values + }) + + logger.info(f"Successfully calculated QED values for {len(df)} molecules") + return df + + +def calculate_qed_series( + smiles_series: Union[List[str], pd.Series], + n_jobs: int = -1, + batch_size: Union[int, str] = "auto", + backend: str = "loky" +) -> pd.Series: + """ + Calculate QED values for a pandas Series or list of SMILES and return as Series. + + Args: + smiles_series: Series or list of SMILES strings + n_jobs (int): Number of parallel jobs + batch_size (int or str): Batch size for parallel processing + backend (str): Joblib backend to use + + Returns: + pd.Series: Series of QED values with the same index as input (if Series) + """ + if isinstance(smiles_series, pd.Series): + smiles_list = smiles_series.tolist() + original_index = smiles_series.index + else: + smiles_list = smiles_series + original_index = None + + # 计算QED值 + results = Parallel( + n_jobs=n_jobs, + batch_size=batch_size, + backend=backend + )(delayed(calculate_single_qed)(smiles) for smiles in smiles_list) + + # 提取QED值（失败的计算返回None） + qed_values = [r[1] if r is not None else None for r in results] + + # 创建Series + if original_index is not None: + return pd.Series(qed_values, index=original_index, name='qed') + else: + return pd.Series(qed_values, name='qed') + + +class QEDCalculator: + """ + A class for calculating QED values with support for parallel processing and caching. + """ + + def __init__(self, n_jobs: int = -1, batch_size: Union[int, str] = "auto"): + """ + Initialize the QEDCalculator. + + Args: + n_jobs (int): Number of parallel jobs. -1 means using all processors + batch_size (int or str): Batch size for parallel processing + """ + self.n_jobs = n_jobs + self.batch_size = batch_size + self.backend = "loky" + + def calculate(self, smiles_list: List[str]) -> pd.DataFrame: + """ + Calculate QED values for a list of SMILES. + + Args: + smiles_list (List[str]): List of SMILES strings + + Returns: + pd.DataFrame: DataFrame with 'smiles' and 'qed' columns + """ + return parallel_qed_calculation( + smiles_list, + n_jobs=self.n_jobs, + batch_size=self.batch_size, + backend=self.backend + ) + + def calculate_series(self, smiles_series: Union[List[str], pd.Series]) -> pd.Series: + """ + Calculate QED values for a pandas Series or list of SMILES and return as Series. + + Args: + smiles_series: Series or list of SMILES strings + + Returns: + pd.Series: Series of QED values + """ + return calculate_qed_series( + smiles_series, + n_jobs=self.n_jobs, + batch_size=self.batch_size, + backend=self.backend + ) + +""" +usage + +from utils.qed_calculator import parallel_qed_calculation, QEDCalculator + +# 方式1：直接使用函数 +smiles_list = ['CCO', 'CCN', 'CCC'] +qed_df = parallel_qed_calculation(smiles_list, n_jobs=-1) + +# 方式2：使用类 +calculator = QEDCalculator(n_jobs=-1) +qed_df = calculator.calculate(smiles_list) + +# 方式3：处理pandas Series +import pandas as pd +smiles_series = pd.Series(['CCO', 'CCN', 'CCC'], name='smiles') +qed_series = calculator.calculate_series(smiles_series) +""" \ No newline at end of file