molfeat

Molecular featurization transforms chemical structures into numerical representations. You can apply hundreds of featurizers, including fingerprints, descriptors, and pretrained transformers, to generate ML-ready feature vectors for QSAR and virtual screening workflows.

Yes, molecular featurization supports easy integration with scikit-learn and PyTorch. This allows you to seamlessly incorporate molecular descriptors and embeddings into end-to-end machine learning pipelines for drug discovery and materials science.

Batch processing SMILES strings for ML model training is best handled using fast batched processing and caching. You can instantiate a featurizer and a MoleculeTransformer to convert SMILES lists into numerical feature vectors efficiently.

Yes, you can use pretrained transformers for molecular embeddings in virtual screening. The featurization suite supports both traditional interpretable descriptors and pretrained transformer embeddings to explore chemical space and train predictive models.

An extensive suite of molecular descriptors and fingerprints is available for clustering and space exploration. These featurizers convert molecular structures into interpretable numerical representations suitable for analyzing chemical space across biology and materials science.