Generating 3D small binding molecules using shape-conditioned diffusion models with guidance

Abstract

Drug development is a critical but notoriously resource- and time-consuming process. Traditional methods, such as high-throughput screening, rely on opportunistic trial and error and cannot ensure optimal precision design. To overcome these challenges, generative artificial intelligence methods have emerged to directly design molecules with desired properties. Here we develop a generative artificial intelligence method DiffSMol for drug discovery that generates 3D small binding molecules based on known ligand shapes. DiffSMol encapsulates ligand shape details within pretrained, expressive shape embeddings and generates binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively using shape guidance to better resemble ligand shapes, and protein pocket guidance to optimize binding affinities. We show that DiffSMol outperforms state-of-the-art methods on benchmark datasets. When generating binding molecules resembling ligand shapes, DiffSMol with shape guidance achieves a success rate 61.4%, substantially outperforming the best baseline (11.2%), meanwhile producing molecules with de novo graph structures. DiffSMol with pocket guidance also outperforms the best baseline in binding affinities by 13.2%, and even by 17.7% when combined with shape guidance. Case studies for two critical drug targets demonstrate very favourable physicochemical and pharmacokinetic properties of generated molecules, highlighting the potential of DiffSMol in developing promising drug candidates.