Comprehensive Review of Contrastive and Generative Self-Supervised Learning for Small Molecular Representation

Drug discovery is a complex and resource-intensive process, where developing effective computational tools to analyze vast and heterogeneous molecular data is paramount. In this landscape, contrastive and generative learning have emerged as two foundational paradigms for molecular representation learning, driving significant advances. The core of these approaches lies in efficiently learning information-dense embeddings by leveraging diverse data modalities, from a molecule's intrinsic 1D, 2D, and 3D structure to its extrinsic context within complex biological networks. The resulting representations provide robust features for a wide array of downstream applications, including molecular property prediction (MPP), interaction analysis, and drug design. This review provides a comprehensive and systematic survey of these two domains. We begin by detailing the essential public databases and benchmarks that support modern research, then conduct an in-depth analysis of contrastive and generative methodologies, first as they apply to single-molecule representations and subsequently as they are adapted for learning from drug interaction networks. Finally, we discuss the pressing challenges and future directions shaping the field, from refining learning strategies to the push toward synergistic, multimodal foundation models. By summarizing the state of the artand providing a structured perspective on current trends and future opportunities, this review serves as a valuable guide for researchers in bioinformatics, chemistry, and artificial intelligence, aiming to foster innovation and accelerate the application of advanced representation learning in modern drug discovery.