structure-based-drug-design

This paper benchmarks general-purpose LLMs against specialized diffusion models for generating binding molecules under 3D spatial constraints, finding that LLMs show promise despite currently lagging behind state-of-the-art approaches.

This paper introduces a novel diffusion-based generative model for structure-based drug design that decouples pocket and ligand representation learning and incorporates multi-scale interaction signals and property-aware optimization to generate developable 3D molecules with improved binding affinity and ADMET properties.

This paper introduces PROBE, a framework that uses LLM agents to iteratively optimize ligands in structure-based drug design by probing pocket-ligand complex responses before editing, achieving state-of-the-art results on CrossDocked2020.