cmxflow

Name: cmxflow
Author: b-shields

by b-shieldsversion 0.4.0i★ –iRuns on your computer

Registry nameio.github.b-shields/cmxflow

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Other

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "cmxflow": {
      "command": "uvx",
      "args": [
        "cmxflow"
      ]
    }
  }
}

Run this in your terminal

claude mcp add cmxflow -- uvx cmxflow

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "cmxflow": {
      "command": "uvx",
      "args": [
        "cmxflow"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "cmxflow": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "cmxflow"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account b-shields, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Otheri

More stars than 0% of the other 32484 servers in this category
Published to the registry more recently than 13% of the other 32484 servers in this category
Basic checks equal to or better than 100% of the other 32484 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is cmxflow safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.4.0) reached the registry 5 months ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Spec Driven Development, Remote Desktop Commander, Recta

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

cmxflow 🧪

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

from cmxflow import Workflow

from cmxflow.sources import MoleculeSourceBlock from cmxflow.operators import ( MoleculeStandardizeBlock, IonizeMoleculeBlock, EnumerateStereoBlock, ConformerGenerationBlock, ) from cmxflow.sinks import MoleculeSinkBlock

Standardize → ionize (pH 6.4–8.4) → enumerate stereo → generate 3D conformers

workflow = 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.

from cmxflow import Workflow

from cmxflow.sources import MoleculeSourceBlock from cmxflow.operators import ConformerGenerationBlock, MoleculeDockBlock from cmxflow.sinks import MoleculeSinkBlock from 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

from cmxflow import Workflow

from cmxflow.sources import MoleculeSourceBlock from cmxflow.operators import MoleculeSimilarityBlock from cmxflow.scores import EnrichmentScoreBlock from 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

claude mcp add cmxflow -- cmxflow-mcp

*"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

pip install cmxflow

MCP server

claude mcp add cmxflow -- cmxflow-mcp

Optional: PyMOL

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

conda install -c conda-forge pymol-open-source

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.

cmxflow

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Otheri

Common questions

Alternatives

Spec Driven Development

Remote Desktop Commander

Recta

README from the maker

cmxflow 🧪

Quick examples

Prepare ligands for docking

Standardize → ionize (pH 6.4–8.4) → enumerate stereo → generate 3D conformers

Dock a congeneric series

Tune a ligand-based virtual screen

Rank a library by 2D similarity to a known active, then tune the

fingerprint end-to-end to maximize enrichment AUC.

Best enrichment AUC: 0.836

{'fingerprint_type': 'morgan', 'similarity_metric': 'sokal', 'radius': 2, 'nbits': 2545}

Or build it conversationally via an LLM agent

What's in the box

Install

MCP server

Optional: PyMOL

Documentation

Project