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Molfeat

Maintainer K-Dense Inc. · Last updated April 1, 2026

Molfeat is a comprehensive Python library for molecular featuri.

Claude CodeOpenClawNanoClawAnalysisReproductionmolfeatchemistrypackagecheminformatics & drug discovery

Original source

K-Dense-AI/claude-scientific-skills

https://github.com/K-Dense-AI/claude-scientific-skills/tree/main/scientific-skills/molfeat

Maintainer
K-Dense Inc.
License
Apache-2.0 license
Last updated
April 1, 2026

Skill Snapshot

Key Details From SKILL.md

2 min

Key Notes

  • Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers. Convert chemical structures (SMILES strings or RDKit molecules) into numerical representations for machine learning tasks including QSAR modeling, virtual screening, similarity searching, and deep learning applications. Features fast parallel processing, scikit-learn compatible transformers, and built-in caching.
  • molfeat[dgl] - GNN models (GIN variants).
  • molfeat[graphormer] - Graphormer models.
  • molfeat[transformer] - ChemBERTa, ChemGPT, MolT5.
  • molfeat[fcd] - FCD descriptors.

Source Doc

Excerpt From SKILL.md

When to Use This Skill

This skill should be used when working with:

  • Molecular machine learning: Building QSAR/QSPR models, property prediction
  • Virtual screening: Ranking compound libraries for biological activity
  • Similarity searching: Finding structurally similar molecules
  • Chemical space analysis: Clustering, visualization, dimensionality reduction
  • Deep learning: Training neural networks on molecular data
  • Featurization pipelines: Converting SMILES to ML-ready representations
  • Cheminformatics: Any task requiring molecular feature extraction

Core Concepts

Molfeat organizes featurization into three hierarchical classes:

1. Calculators (molfeat.calc)

Callable objects that convert individual molecules into feature vectors. Accept RDKit Chem.Mol objects or SMILES strings.

Use calculators for:

  • Single molecule featurization
  • Custom processing loops
  • Direct feature computation

Example:

Use cases

  • **Molecular machine learning**: Building QSAR/QSPR models, property prediction.
  • **Virtual screening**: Ranking compound libraries for biological activity.
  • **Similarity searching**: Finding structurally similar molecules.
  • **Chemical space analysis**: Clustering, visuali.

Not for

  • Do not rely on this catalog entry alone for installation or maintenance details.

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