molfeat

Topics in the source

• Molfeat - Molecular Featurization Hub
• Overview
• When to Use This Skill
• Installation
• Core Concepts
• 1. Calculators (molfeat.calc)
• 2. Transformers (molfeat.trans)
• 3. Pretrained Transformers (molfeat.trans.pretrained)

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. 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 Optional dependencies for specific featurizers: molfeat[dgl] - GNN models (GIN variants) molfeat[graphormer] - Graphormer models molfeat[transformer] - ChemBERTa, ChemGPT, MolT5 molfeat[fcd] - FCD descriptors molfeat[map4]

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