Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@cmxflow Run Lipinski's rule-of-five on ligands.csv"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
Related MCP server: SciAgentKit
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
pip install cmxflowclaude mcp add cmxflow -- cmxflow-mcpRequired only for the view_structures MCP tool (3D visualization):
conda install -c conda-forge pymol-open-sourceUsing with Claude — agent transcripts
examples/basic_usage.ipynb — full tutorial
examples/docking/docking.ipynb — docking walkthrough (ILS, scaffold-indexed, and template modes)
MIT licensed. See CONTRIBUTING.md and RELEASING.md.
This server cannot be installed
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
AI-powered bioprotocol optimization — generate, search, and manage lab protocols via MCP
Create, browse, remix, collaborate on, and run durable AI workflow nodes from MCP hosts.
MCP Hub: AI service discovery, per-user OAuth, and multi-service workflow orchestration
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/b-shields/cmxflow'
If you have feedback or need assistance with the MCP directory API, please join our Discord server