Ligand Generation

Ligand generation, a crucial aspect of drug discovery, aims to computationally design molecules that bind to specific protein targets. Current research heavily utilizes deep generative models, particularly diffusion models and variational autoencoders, often incorporating equivariance to handle the rotational symmetry of molecules and employing multi-objective optimization to guide the generation towards molecules with desirable properties like high binding affinity and synthetic accessibility. These advancements leverage diverse protein representations (e.g., sequence, 3D structure) and sophisticated training strategies to improve the quality and efficiency of generated ligands, accelerating the drug development process. The resulting improvements in ligand generation promise to significantly impact drug discovery by providing a more efficient and effective approach to identifying potential drug candidates.