Guided multi-objective generative AI to enhance structure-based drug design.

ABSTRACT: Generative AI has the potential to revolutionize drug discovery. Yet, despite recent advances in deep learning, existing models cannot generate molecules that satisfy all desired physicochemical properties. Herein, we describe IDOLpro, a novel generative chemistry AI combining diffusion with multi-objective optimization for structure-based drug design. Differentiable scoring functions guide the latent variables of the diffusion model to explore uncharted chemical space and generate novel ligands in silico, optimizing a plurality of target physicochemical properties. We demonstrate our platform's effectiveness by generating ligands with optimized binding affinity and synthetic accessibility on two benchmark sets. IDOLpro produces ligands with binding affinities over 10-20% higher tha

SUBMITTER: Kadan A 

PROVIDER: S-EPMC12127851 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature