Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models with Guidance.

ABSTRACT: Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSMol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geometric details of ligand shapes within pre-trained, expressive shape embeddings and then generates new binding molecules through a diffusion model. DiffSMol further modifies the generated 3D structures iteratively via shape guidance to better resemble the ligand shapes. It also tailors the generated molecules toward optimal binding affinities under the guidance of protein pockets. Here, we show that DiffSMol outperforms the state-of-the-art methods on benchmark datasets. When g

SUBMITTER: Chen Z 

PROVIDER: S-EPMC12622471 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature