  Generative AI has the potential to revolutionize drug discovery. Yet, despite
recent advances in machine learning, existing models cannot generate molecules
that satisfy all desired physicochemical properties. Herein, we describe
IDOLpro, a novel generative chemistry AI combining deep diffusion with
multi-objective optimization for structure-based drug design. The latent
variables of the diffusion model are guided by differentiable scoring functions
to explore uncharted chemical space and generate novel ligands in silico,
optimizing a plurality of target physicochemical properties. We demonstrate its
effectiveness by generating ligands with optimized binding affinity and
synthetic accessibility on two benchmark sets. IDOLpro produces ligands with
binding affinities over 10% higher than the next best state-of-the-art on each
test set. On a test set of experimental complexes, IDOLpro is the first to
surpass the performance of experimentally observed ligands. IDOLpro can
accommodate other scoring functions (e.g. ADME-Tox) to accelerate hit-finding,
hit-to-lead, and lead optimization for drug discovery.
