molfeat-molecular-featurization
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
npx skills add BioTender-max/awesome-bio-agent-skills --skill molfeat-molecular-featurization --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Molfeat — Molecular Featurization Hub ## Overview Molfeat is a comprehensive Python library for molecular featurization that unifies 100+ pre-trained embeddings and hand-crafted featurizers under a scikit-learn compatible API. Convert SMILES strings into numerical representations (fingerprints, descriptors, deep learning embeddings) for QSAR modeling, virtual screening, similarity searching, and chemical space analysis. ## When to Use - Building QSAR/QSPR models requiring molecular features as input - Virtual screening — ranking compound libraries by predicted activity - Similarity searching against molecular databases - Chemical space analysis — clustering, visualization, dimensionality reduction - Deep learning on molecules using pretrained embeddings (ChemBERTa, GIN) - Featurization pipelines integrating with scikit-learn or PyTorch - Comparing multiple molecular representations for benchmarking - For molecular manipulation and filtering use datamol instead; for substructure-based molecular operations use rdkit-molecular-toolkit ## Prerequisites ```bash uv pip install molfeat # Optional extras for specific featurizer types uv pip install "molfeat[transformer]" # ChemBERTa, Che
- Overview
- When to Use
- Prerequisites
- Quick Start
- Key Concepts
- Architecture: Calculator → Transformer → Store
- Featurizer Selection Guide
- State Persistence
- Core API
- 1. Fingerprint Calculators
- 2. Descriptor Calculators
- 3. Pharmacophore & Shape Calculators
- 4. Batch Processing with Transformers
- 5. Pretrained Model Embeddings
uv pip install molfeat Optional extras for specific featurizer types uv pip install "molfeat[transformer]" # ChemBERTa, ChemGPT, MolT5 uv pip install "molfeat[dgl]" # GIN graph neural networks uv pip install "molfeat[graphormer]" # Graphormer models uv pip install "molfeat[fcd]" # FCD descriptors uv pip install "molfeat[map4]" # MAP4 fingerprints uv pip install "molfeat[all]" # All dependencies
What does the molfeat-molecular-featurization skill do?
Molecular featurization hub (100+ featurizers) for ML. SMILES to fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained embeddings (ChemBERTa, GIN, Graphormer), pharmacophores. Scikit-learn compatible with parallelization/caching. For QSAR, virtual screening, similarity, and molecular DL.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill molfeat-molecular-featurization --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.