deepchem

To predict molecular properties, use molecular ML workflows to load chemical data, apply featurization, split datasets, train models, and evaluate results. This toolkit supports small and large datasets across cheminformatics tasks including toxicity and property prediction.

Scaffold splitting is a dataset partitioning technique used in molecular ML to group molecules by their core chemical structures, ensuring that training and test sets are structurally distinct and reducing data leakage during model evaluation.

Transfer learning for drug discovery applies pre-trained transformers and PyTorch-based models to new molecular datasets. Leverage MoleculeNet benchmarks and pretrained options like ChemBERTa or GROVER to fine-tune models on specific property prediction tasks.

Yes, graph neural networks like MolGraphConvFeaturizer, DMPNN, and GROVER are supported for molecular property prediction. These graph-based featurizers convert molecular structures into graph representations suitable for training PyTorch-based models.

The best featurization method depends on your data: use CircularFingerprint for structural keys, MolGraphConvFeaturizer for graph neural networks, SmilesToSeq for sequence-based models, or pretrained options like GROVER and ChemBERTa for transfer learning.

Yes, it supports MoleculeNet benchmarks for evaluating molecular ML models. Run quick benchmarks on small datasets using GraphConv featurization, or evaluate trained models across established datasets for toxicity, materials-property, and protein sequence analysis tasks.