Molfeat (Claude Skill)

Claude skill providing Python recipes for Molfeat, a featurizer hub that unifies 100+ molecular representations — ECFP / MACCS / RDKit fingerprints, 2D/3D descriptors, molecular graphs, and pretrained embeddings (ChemBERTa, MolBERT, Uni-Mol) — behind a single scikit-learn-compatible transformer API.

   
Type Claude Skill
Supplier K-Dense Inc. (community OSS)
Availability GA — actively maintained 2025–2026
Pricing Free / OSS skill (MIT collection); Molfeat itself is Apache-2.0
Capabilities Read/Write — Claude executes Molfeat via the Bash/Python tool

How to install

  • Also packaged in the SciAgent-Skills collection (jaechang-hits (community OSS, CC BY 4.0)): clone jaechang-hits/SciAgent-Skills and run /plugin install sciagent-skills in Claude Code (or copy skills/structural-biology-drug-discovery/molfeat-molecular-featurization into ~/.claude/skills/).
  • Claude Code / Claude.ai — Skills CLI (recommended): 
    npx skills add K-Dense-AI/scientific-agent-skills

    Installs the K-Dense collection; enable the molfeat skill when prompted (also works in Cursor/Codex via the Agent Skills spec; requires Node ≥ 18).

  • Claude Code / Claude Desktop — manual clone: 
    git clone https://github.com/K-Dense-AI/scientific-agent-skills
cp -r scientific-agent-skills/skills/molfeat ~/.claude/skills/
pip install molfeat

Project-scoped alternative: copy into .claude/skills/ instead of ~/.claude/skills/.

What it does

SKILL.md with recipes for:

  • Classical fingerprints — ECFP / Morgan, MACCS, RDKit, pharmacophore
  • 2D/3D descriptors — constitutional, topological, electronic
  • Graph-based representations — molecular graphs, line graphs
  • Pre-trained embeddings — ChemBERTa, MolBERT, Uni-Mol via the Molfeat hub
  • Unified transformer API for fit / transform use in scikit-learn / PyTorch pipelines
  • Caching and parallel processing for virtual-screening-scale compound sets

Primary use cases: Featurization for QSAR / property-prediction models, virtual screening, molecular similarity search, preparing input tensors for deep-learning chemistry models.

Notes

Skill is documentation plus Python recipes — Claude executes Molfeat locally via Bash/Python. Pairs naturally with the catalog’s rdkit-skill and datamol pages for end-to-end lead-optimisation pipelines. Pretrained-model featurizers may download weights on first use; expect a one-off network hop.

Sources

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