Structure-Conditioned Generative Models for De Novo Ligand Generation: A Pharmacophore Assessment

Deep generative models show promise for de novo molecular design, especially pocket-conditioned conditional generation methods that output small-molecule ligands in their predicted binding pose with high shape complementarity. However, recent work demonstrates these models still fail to generate chemically valid and synthetically accessible ligands. This paper provides further insight into these methods and their generated molecules through analysis of pharmacophore features commonly used in structure-based and ligand-based drug discovery. We specifically assess the generated distribution of hydrogen bond donors, acceptors, and aromatic rings from deep generative methods on three well-studied protein targets: adenosine A2a receptor, cyclin-dependent kinase 2, and the main protease of SARS-CoV-2. Our results find autoregressive approaches better recapitulate the expected spatial distribution of pharmacophore features compared to diffusion-based models. The analysis presented here highlights current limitations in deep generative models for 3D design, while suggesting new directions to realistically aid structure-based design.