Based Docking

Based docking, a crucial technique in drug discovery, aims to predict how small molecules bind to proteins, informing the design of new drugs. Current research focuses on improving the accuracy and generalizability of machine learning (ML)-based docking methods, employing architectures like transformers and equivariant neural networks to better leverage 3D spatial and graph-level information. Large-scale datasets are being developed to train and benchmark these models, addressing limitations of previous approaches. These advancements hold significant promise for accelerating drug discovery by enabling more efficient and accurate virtual screening and de novo drug design.