PMODiff: Physics-Informed Multi-Objective Optimization Diffusion Model for Protein-Specific 3D Molecule Generation
Yaoxiang Zhang, Shuang Wang, Junjie Ma, Ze Zhang, Tao Song · Journal of Chemical Information and Modeling · 2025
3D generative models have shown great potential in structure-based drug design for generating ligands tailored to specific protein binding pockets. However, existing methods primarily emphasize ligand-target geometric interactions and binding affinity prediction, often overlooking intrinsic physicochemical principles driving protein–ligand interactions as well as critical pharmaceutical properties, such as drug-likeness and synthetic accessibility. To address these limitations, PMODiff ( P hysics-Informed M ulti-Objective O ptimization Diff usion Model) integrates a physics-informed component into the denoising phase, minimizing protein–ligand interaction energy modeled by a simplified Lennard-Jones potential, thus generating conformations aligned with essential physicochemical constraints. In addition, pretrained networks guide the sampling process toward ligands exhibiting high affinity, favorable drug-likeness, and synthetic accessibility, thus addressing multiobjective optimization challenges in practical drug development. Experimental results on the CrossDocked2020 data set indicate that PMODiff generates more realistic 3D structures with higher binding affinity, achieving an average Vina Score of −7.44. This performance represents a 13% improvement over existing methods, highlighting the potential of PMODiff for more comprehensive drug design.