{"ID":2840090,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.14559","arxiv_id":"2511.14559","title":"Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models","abstract":"Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformational rearrangements induced by ligand binding, limiting their applicability in practical drug discovery. Here, we propose Apo2Mol, a diffusion-based generative framework for 3D molecule design that explicitly accounts for conformational flexibility in protein binding pockets. To support this, we curate a dataset of over 24,000 experimentally resolved apo-holo structure pairs from the Protein Data Bank, enabling the characterization of protein structure changes associated with ligand binding. Apo2Mol employs a full-atom hierarchical graph-based diffusion model that simultaneously generates 3D ligand molecules and their corresponding holo pocket conformations from input apo states. Empirical studies demonstrate that Apo2Mol can achieve state-of-the-art performance in generating high-affinity ligands and accurately capture realistic protein pocket conformational changes.","short_abstract":"Deep generative models are rapidly advancing structure-based drug design, offering substantial promise for generating small molecule ligands that bind to specific protein targets. However, most current approaches assume a rigid protein binding pocket, neglecting the intrinsic flexibility of proteins and the conformatio...","url_abs":"https://arxiv.org/abs/2511.14559","url_pdf":"https://arxiv.org/pdf/2511.14559v1","authors":"[\"Xinzhe Zheng\",\"Shiyu Jiang\",\"Gustavo Seabra\",\"Chenglong Li\",\"Yanjun Li\"]","published":"2025-11-18T15:01:27Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.AI\",\"cs.LG\",\"q-bio.QM\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}