TopMT-GAN: a 3D topology-driven generative model for efficient and diverse structure-based ligand design

Abstract

Recent advancements in 3D structure-based molecular generative models have shown promise in expediting the hit discovery process in drug design. Despite their potential, efficiently generating a focused library of candidate molecules that exhibit both effective interactions and structural diversity at a large scale remains a significant challenge. Moreover, current studies often lack comprehensive comparisons to high-throughput virtual screening methods, resulting in insufficient evaluation of their effectiveness. In this study, we introduce Topology Molecular Type assignment (TopMT-GAN), a novel approach using Generative Adversarial Networks (GANs) for direct structure-based design. TopMT-GAN employs a two-step strategy: constructing 3D molecular topologies within a protein pocket with one GAN, followed by atom and bond type assignment with a second GAN. This integrated approach enables TopMT-GAN to efficiently generate diverse and potent ligands with precise 3D poses for specific protein pockets. When tested on five diverse protein pockets, TopMT-GAN exhibits promising and robust performance, demonstrating a potential enrichment of up to 46 000 fold compared to traditional high-throughput virtual screening methods. This highlights its potential as a powerful tool in early-stage drug discovery, such as hit and lead generation.