Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design

Significance Structure-based drug design is a critical field in chemistry and biology, and recent advancements in machine learning have significantly enhanced the generation of ligands with high binding affinities for target proteins. However, existing machine learning methods have been overlooking a crucial physical prior: Atoms must maintain a minimum pairwise distance to avoid atomic collisions. We address this issue by introducing a learning approach that incorporates motivated geometric constraints to improve the physical plausibility and binding affinity of generated molecular structures. Our results demonstrate improved molecular realism and practical benefits for drug design tasks.