General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design

Structure-based drug design (SBDD) aims to generate ligands that bind strongly and specifically to target protein pockets. Recent diffusion models have advanced SBDD by capturing the distributions of atomic positions and types, yet they often underemphasize binding affinity control during generation. To address this limitation, we introduce textbftextnormalBADGER, a general binding-affinity guidance framework for diffusion models in SBDD. textnormalBADGER incorporates binding affinity awareness through two complementary strategies: (1) textitclassifier guidance, which applies gradient-based affinity signals during sampling in a plug-and-play fashion, and (2) textitclassifier-free guidance, which integrates affinity conditioning directly into diffusion model training. Together, these approaches enable controllable ligand generation guided by binding affinity. textnormalBADGER can be added to any diffusion model and achieves up to a 60% improvement in ligand--protein binding affinity of sampled molecules over prior methods. Furthermore, we extend the framework to multi-constraint diffusion guidance, jointly optimizing for binding affinity, drug-likeness (QED), and synthetic accessibility (SA) to design realistic and synthesizable drug candidates.