cmxflow

io.github.b-shields由 GitHub 账号 b-shields 发布v0.4.0更新于 Oct 24, 2026

Composable, tunable cheminformatics workflows: library preparation, clustering, docking, and more.

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服务详情

标识符: io.github.b-shields/cmxflow
传输: STDIO
运行时: Desktop only
信任: Verified
发布于: Sep 16, 2026

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概览

AI 生成的概览

cmxflow 是一个本地 MCP 服务器，让助手构建、运行并调优化合物信息学工作流，如配体准备、聚类、对接和虚拟筛选。

可以做什么: 用可组合模块搭建工作流，涵盖分子来源、转换、过滤、聚类、评分和对接，并在分子库上运行以生成准备好的或对接后的结构文件。通过贝叶斯优化端到端调整工作流参数并比较结果。通过序列化保存、加载和复用工作流。安装 PyMOL 后可查看分子的三维结构。
适用场景: 适合希望以对话方式组装并执行计算化学流程的场景，例如为对接准备配体、对同系列化合物进行对接，或调优基于配体的虚拟筛选，而无需自己编写流程代码。
运行要求: 作为本地进程通过 stdio 运行，从 PyPI 安装 cmxflow，并用 cmxflow-mcp 命令启动，例如通过 uvx。需要 Python 环境。三维结构查看工具额外需要 PyMOL（pymol-open-source）。未声明账户、API 密钥或请求头。

安装前请注意

工作流会写出准备好的或对接后的结构文件等输出文件，优化过程会运行多次试验，可能消耗较多算力。三维查看工具依赖可选的 PyMOL 安装。在重要数据上运行前请检查生成的工作流。

安装

在 SourceWeft 中

打开控制台中的 cmxflow，将其添加到工作区。
为需要使用其工具的对话启用该服务。

Desktop only，通过 STDIO。 STDIO 服务会启动本地进程，因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照仓库中的启动说明。

README

cmxflow 🧪

[Docs] [CI] [codecov] [Python 3.11+] [Code style: black] [License: MIT]

Build cheminformatics and computational chemistry pipelines with composable blocks. Tune end-to-end with Bayesian Optimization. Or ask an LLM agent to do it.

Quick examples

Prepare ligands for docking

python

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import (    MoleculeStandardizeBlock,    IonizeMoleculeBlock,    EnumerateStereoBlock,    ConformerGenerationBlock,)from cmxflow.sinks import MoleculeSinkBlock
# Standardize → ionize (pH 6.4–8.4) → enumerate stereo → generate 3D conformersworkflow = Workflow()workflow.add(    MoleculeSourceBlock(),    MoleculeStandardizeBlock(),    IonizeMoleculeBlock(),    EnumerateStereoBlock(),    ConformerGenerationBlock(),    MoleculeSinkBlock(),)workflow("library.smi", "prepared.sdf")

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import (    MoleculeStandardizeBlock,    IonizeMoleculeBlock,    EnumerateStereoBlock,    ConformerGenerationBlock,)from cmxflow.sinks import MoleculeSinkBlock
# Standardize → ionize (pH 6.4–8.4) → enumerate stereo → generate 3D conformersworkflow = Workflow()workflow.add(    MoleculeSourceBlock(),    MoleculeStandardizeBlock(),    IonizeMoleculeBlock(),    EnumerateStereoBlock(),    ConformerGenerationBlock(),    MoleculeSinkBlock(),)workflow("library.smi", "prepared.sdf")

Dock a congeneric series

Pure-Python docking. Free docking is the default (index_poses=False); scaffold-indexed mode caches poses by Bemis–Murcko scaffold for ~3× faster throughput on congeneric series with consistent pose alignment.

python

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import ConformerGenerationBlock, MoleculeDockBlockfrom cmxflow.sinks import MoleculeSinkBlockfrom cmxflow.utils.parallel import make_parallel
workflow = Workflow()workflow.add(    MoleculeSourceBlock(),    ConformerGenerationBlock(),    make_parallel(        MoleculeDockBlock(            receptor="receptor.pdb",            site_reference="crystal_ligand.sdf",            index_poses=True,  # omit for free docking        )    ),    MoleculeSinkBlock(),)workflow("library.smi", "docked.sdf")

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import ConformerGenerationBlock, MoleculeDockBlockfrom cmxflow.sinks import MoleculeSinkBlockfrom cmxflow.utils.parallel import make_parallel
workflow = Workflow()workflow.add(    MoleculeSourceBlock(),    ConformerGenerationBlock(),    make_parallel(        MoleculeDockBlock(            receptor="receptor.pdb",            site_reference="crystal_ligand.sdf",            index_poses=True,  # omit for free docking        )    ),    MoleculeSinkBlock(),)workflow("library.smi", "docked.sdf")

Tune a ligand-based virtual screen

python

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import MoleculeSimilarityBlockfrom cmxflow.scores import EnrichmentScoreBlockfrom cmxflow.opt import Optimizer
# Rank a library by 2D similarity to a known active, then tune the# fingerprint end-to-end to maximize enrichment AUC.workflow = Workflow()workflow.add(    MoleculeSourceBlock(),    MoleculeSimilarityBlock(queries="crystal_ligand.sdf"),    EnrichmentScoreBlock(target="active"),)
opt = Optimizer(workflow, "benchmark.csv")opt.optimize(n_trials=30, direction="maximize")
print(f"Best enrichment AUC: {opt.best_score:.3f}")print(opt.best_params)# Best enrichment AUC: 0.836# {'fingerprint_type': 'morgan', 'similarity_metric': 'sokal', 'radius': 2, 'nbits': 2545}

from cmxflow import Workflowfrom cmxflow.sources import MoleculeSourceBlockfrom cmxflow.operators import MoleculeSimilarityBlockfrom cmxflow.scores import EnrichmentScoreBlockfrom cmxflow.opt import Optimizer
# Rank a library by 2D similarity to a known active, then tune the# fingerprint end-to-end to maximize enrichment AUC.workflow = Workflow()workflow.add(    MoleculeSourceBlock(),    MoleculeSimilarityBlock(queries="crystal_ligand.sdf"),    EnrichmentScoreBlock(target="active"),)
opt = Optimizer(workflow, "benchmark.csv")opt.optimize(n_trials=30, direction="maximize")
print(f"Best enrichment AUC: {opt.best_score:.3f}")print(opt.best_params)# Best enrichment AUC: 0.836# {'fingerprint_type': 'morgan', 'similarity_metric': 'sokal', 'radius': 2, 'nbits': 2545}

The four fingerprint parameters above are searched automatically — every block exposes its mutable parameters to the optimizer.

Or build it conversationally via an LLM agent

"How many of the molecules in library.csv pass Lipinski's rules?"

"I need to build a ligand-based virtual screening workflow. I'm not sure if 2D or 3D is better. Can you optimize two workflows?"

"Dock the molecules in hits.csv against receptor.pdb with crystal_ligand.sdf as a reference."

The agent can build, run, and optimize workflows. See Using with Claude for full transcripts.

What's in the box

15+ blocks for sourcing, transforming, filtering, clustering, scoring, and docking molecules
Bayesian optimization of pipeline parameters via Optuna
Parallel execution for compute-heavy blocks (conformer generation, docking)
Workflow serialization for save / load / reuse
An MCP server with five tools: build_workflow, run_workflow, optimize_workflow, manage_workflows, view_structures

Install

MCP server

Optional: PyMOL

Required only for the view_structures MCP tool (3D visualization):

Documentation

Docs site
Block catalog
Using with Claude — agent transcripts
examples/basic_usage.ipynb — full tutorial
examples/docking/docking.ipynb — docking walkthrough (ILS, scaffold-indexed, and template modes)

Project

MIT licensed. See CONTRIBUTING.md and RELEASING.md.

来源：README.md，提交 c2960bc

工具

SourceWeft 尚未在沙箱中启动过该软件包，因此其工具未知。

版本历史

v0.4.0最新Sep 16, 2026

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