Pharmacophore-oriented 3D molecular generation toward efficient feature-customized drug discovery.

Abstract

Molecular generation is a cutting-edge technology with the potential to revolutionize intelligent drug discovery. However, currently reported ligand-based or structure-based molecular generation methods remain unpractical for real-world drug discovery. Here we propose an explicit pharmacophore-oriented 3D molecular generation method, termed PhoreGen. PhoreGen employs asynchronous perturbations and updates on both atomic and bond information, coupled with a message-passing mechanism that incorporates prior knowledge of ligand-pharmacophore mapping during the diffusion-denoising process. Evaluations revealed that PhoreGen efficiently generates 3D molecules well aligned with pharmacophores, maintaining good chemical reasonability, diversity, drug-likeness and binding affinity and, importantly, produces feature-customized molecules at high frequency. By using PhoreGen, we successfully identified new bicyclic boronate inhibitors of evolved metallo-β-lactamase and serine-β-lactamases, which potentiate meropenem against clinically isolated superbugs. Moreover, we identified inhibitors of metallo-nicotinamidases, emerging targets for insecticides. This work explores an explicitly constrained mode for molecular generation and demonstrates its potential in feature-customized drug discovery.