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Composable, tunable cheminformatics workflows: library preparation, clustering, docking, and more.
Composable, tunable cheminformatics workflows: library preparation, clustering, docking, and more.
Valid MCP server (0 strong, 2 medium validity signals). No known CVEs in dependencies. Imported from the Official MCP Registry. 1 finding(s) downgraded by scanner intelligence.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-b-shields-cmxflow": {
"args": [
"--from",
"cmxflow",
"cmxflow-mcp",
"cmxflow"
],
"command": "uvx"
}
}
}From the project's GitHub README.
Build cheminformatics and computational chemistry pipelines with composable blocks. Tune end-to-end with Bayesian Optimization. Or ask an LLM agent to do it.
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")
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")
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.
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.
build_workflow, run_workflow, optimize_workflow, manage_workflows, view_structurespip install cmxflow
claude mcp add cmxflow -- cmxflow-mcp
Required only for the view_structures MCP tool (3D visualization):
conda install -c conda-forge pymol-open-source
examples/basic_usage.ipynb — full tutorialexamples/docking/docking.ipynb — docking walkthrough (ILS, scaffold-indexed, and template modes)MIT licensed. See CONTRIBUTING.md and RELEASING.md.
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