Virtual Screening

Virtual screening is a computational technique used in drug discovery to identify promising drug candidates from vast chemical libraries by predicting their binding affinity to target proteins. Current research emphasizes improving the efficiency and accuracy of virtual screening through the development of advanced machine learning models, including graph neural networks, transformers, and generative models like Generative Flow Networks and Variational Autoencoders, often incorporating contrastive learning and active learning strategies. These advancements aim to reduce the computational cost and time required for screening, ultimately accelerating the drug discovery process and enabling the exploration of larger chemical spaces. The impact of these improvements is significant, potentially leading to faster identification of drug candidates and more efficient drug development.