Based Virtual Screening

Based virtual screening (VS) uses machine learning to identify promising drug candidates from vast chemical libraries without relying on detailed protein structure information. Current research emphasizes developing advanced models, including graph neural networks, diffusion models, and contrastive learning frameworks, to improve the accuracy and efficiency of ligand-based VS, often incorporating techniques like active learning and transfer learning to enhance sample efficiency and generalizability across diverse targets. These advancements are crucial for accelerating drug discovery by significantly reducing the time and cost associated with traditional screening methods, ultimately leading to faster development of new therapeutics.