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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.