Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models

• URL: http://arxiv.org/abs/2511.14559v1
• Date: Tue, 18 Nov 2025 15:01:27 GMT
• Title: Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models
• Authors: Xinzhe Zheng, Shiyu Jiang, Gustavo Seabra, Chenglong Li, Yanjun Li,
• Abstract summary: We propose Apo2Mol, a diffusion-based generative framework for 3D molecule design.<n>Apo2Mol explicitly accounts for conformational flexibility in protein binding pockets.<n>It can achieve state-of-the-art performance in generating high-affinity and accurately capture realistic protein pocket conformational changes.
• Score: 10.08056210949295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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