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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.