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 on

SUBMITTER: Wang S 

PROVIDER: S-EPMC11726321 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature