cmxflow 0.1.0

A Python package for building and optimizing cheminformatics workflows using Bayesian optimization and LLM agents

cmxflow 🧪

Composable cheminformatics workflows with Bayesian optimization.

Overview 🔬

cmxflow is a Python framework for building and optimizing cheminformatics pipelines. Chain together molecular operations as blocks, then let Bayesian optimization find the best parameters for your task.

Read the full documentation →

Two Usage Modes ⚗️

cmxflow is designed to work both as:

1. An Agentic Tool - via MCP (Model Context Protocol) server, allowing LLM agents to build and optimize workflows conversationally
2. A Programmatic API - for direct Python usage in scripts and notebooks

Block Types 🧬

Workflows are built from four types of blocks:

Block Type	Purpose
SourceBlock	Read molecules from files (SDF, SMILES, CSV, Parquet)
Block	Transform molecules (1:1 or N:M)
SinkBlock	Write molecules to files
ScoreBlock	Compute optimization objective

Example Operators 💊

Block	Purpose
MoleculeStandardizeBlock	Standardize molecules (metals, salts, charges, tautomers)
MoleculeDeduplicateBlock	Remove duplicate molecules by canonical SMILES
RDKitBlock	Apply any RDKit method (descriptors, transformations)
SubstructureFilterBlock	Filter by SMARTS patterns or catalogs (PAINS, BRENK, etc.)
PropertyFilterBlock	Filter molecules by property conditions
PropertyHeadBlock	Select top N molecules by property
PropertyTailBlock	Select bottom N molecules by property
MoleculeSimilarityBlock	Compute 2D fingerprint similarity
Molecule3DSimilarityBlock	Compute 3D shape similarity
IonizeMoleculeBlock	Generate pH-dependent ionization states
EnumerateStereoBlock	Enumerate all stereoisomers
ConformerGenerationBlock	Generate 3D conformers (ETKDGv3)
MoleculeAlignBlock	Align molecules to 3D reference
MoleculeDockBlock	Dock into protein binding pocket
RepresentativeClusterBlock	Cluster molecules by fingerprint similarity (leader algorithm)

Example Score Blocks 📊

ScoreBlock	Purpose
EnrichmentScoreBlock	Enrichment AUC for virtual screening
AverageScoreBlock	Mean of a molecular property
ShapeOverlayScoreBlock	Average 3D shape similarity
ClusterScoreBlock	Cluster quality from representative clustering

Features 🚀

• Composable Pipelines - Chain blocks with workflow.add()
• Bayesian Optimization - Find optimal parameters via Optuna
• Parallel Execution - make_parallel() for compute-intensive blocks
• Mutable Parameters - Categorical, Integer, and Continuous types
• Serialization - save_workflow() and load_workflow() for persistence
• MCP Server - Agentic workflow building via build_workflow, run_workflow, optimize_workflow

Environment Variables 🔧

Variable	Default	Description
CMXFLOW_WORKER_TIMEOUT	30	Seconds to wait for a single parallel worker before treating it as failed. Set to 0 to disable the timeout. Applies to all make_parallel() and @parallel blocks.

Getting Started 📖

See examples/basic_usage.ipynb for a complete tutorial covering:

• Building your first workflow
• 2D similarity search
• Mutable parameters and optimization
• Parallel execution
• Analyzing results with Optuna

The tutorial uses the ABL1 kinase benchmark from the wonderful DUD-E database.

Installation 🛠️

pip install cmxflow

MCP Server

To use cmxflow as an agentic tool with Claude Code:

claude mcp add cmxflow -- cmxflow-mcp

Optional Dependencies

PyMOL — Required only for 3D structure visualization (view_structures MCP tool). Install via conda:

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

All other functionality works without PyMOL.

Contributing 🤝

Contributions are welcome! This is a side project, so reviews may take some time, but PRs are appreciated.

Before Submitting

1. Open an issue first for significant changes to discuss the approach
2. Fork the repo and create a feature branch from main
3. Follow the code style - run mypy, black, and ruff before committing (or install provided precommit hooks)

PR Requirements

• Clear description of the bug fixed or feature added
• Minimal reproducible example demonstrating the change
• Tests covering new functionality (pytest)
• Type hints for all new code
• Docstrings following Google conventions

Development Setup

conda config --set solver libmamba
conda env create -f conda.yml
conda activate cmxflow
poetry install
pre-commit install  # Ensures formatting/linting on commit

Running Tests

pytest tests/

Releases

Releases are published to PyPI automatically when a pull request is merged into main with a version bump tag in the PR title:

Tag in PR title	Version bump	Example
[patch]	Bug fixes, docs (0.1.0 → 0.1.1)	Fix conformer bug [patch]
[minor]	New features, backwards-compatible (0.1.0 → 0.2.0)	Add ProtonationBlock [minor]
[major]	Breaking changes (0.1.0 → 1.0.0)	Redesign block API [major]

PRs without a tag merge normally without triggering a release.

License 📄

MIT