cmxflow

io.github.b-shields由 GitHub 帳號 b-shields 發布v0.4.0更新於 Oct 24, 2026

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

已驗證STDIO僅桌面資料與分析 AI 與機器學習

服務詳細資訊

識別碼: io.github.b-shields/cmxflow
傳輸: STDIO
執行環境: Desktop only
信任: Verified
發布於: Sep 16, 2026

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概覽

AI 產生的概覽

cmxflow 是本地 MCP 伺服器，讓助理建立、執行並調校化學資訊學工作流程，例如配體準備、分群、對接與虛擬篩選。

可以做什麼: 以可組合區塊建立工作流程，涵蓋分子來源、轉換、過濾、分群、評分與對接，並在分子庫上執行以產生備妥或對接後的結構檔。透過貝氏最佳化端到端調整工作流程參數並比較結果。透過序列化儲存、載入與重複使用工作流程。安裝 PyMOL 後可檢視分子的三維結構。
適用情境: 適合希望以對話方式組裝並執行計算化學流程的情境，例如為對接準備配體、對同系列化合物進行對接，或調校以配體為基礎的虛擬篩選，而不必自行撰寫流程程式碼。
執行需求: 以本機程序透過 stdio 執行，從 PyPI 安裝 cmxflow，並以 cmxflow-mcp 指令啟動，例如透過 uvx。需要 Python 環境。三維結構檢視工具另需 PyMOL（pymol-open-source）。未宣告帳號、API 金鑰或標頭。

安裝前請注意

工作流程會寫出備妥或對接後的結構檔等輸出檔，最佳化過程會執行多次試驗，可能耗費較多運算資源。三維檢視工具依賴選用的 PyMOL 安裝。在重要資料上執行前請先檢查產生的流程。

安裝

在 SourceWeft 中

開啟儀表板中的 cmxflow，將其新增到工作區。
為需要使用其工具的對話啟用該服務。

Desktop only，透過 STDIO。 STDIO 服務會啟動本機處理程序，因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照儲存庫中的啟動說明。

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.

來源：README.md，提交 c2960bc

工具

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版本歷史

v0.4.0最新Sep 16, 2026

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