Molecular Representation Learning

Molecular representation learning studies how to encode molecules and biomolecules so models can recognize chemical structure, predict properties, and support tasks such as drug screening and molecular design. Approaches range from fingerprints and molecular graphs to SMILES language models, 3D equivariant networks, fragment-based methods, and multimodal models combining structure with spectra, text, or biological measurements. Self-supervised and contrastive pretraining are common ways to learn from unlabeled structures, while supervised models use task-specific labels for properties such as solubility, binding affinity, or toxicity. A central challenge is reliable transfer to new chemical scaffolds and biological settings: larger pretrained models do not always beat simpler fingerprints or descriptors, and results depend strongly on data splits, representation choices, and whether physical and chemical information is preserved.