Apo2Mol: 3D Molecule Generation via Dynamic Pocket-Aware Diffusion Models
This addresses a key limitation in structure-based drug design for pharmaceutical researchers by enabling more accurate molecule generation, though it is incremental in improving existing diffusion models.
The paper tackled the problem of generating 3D molecules for drug design by accounting for protein flexibility, which is often neglected in rigid-pocket assumptions, and achieved state-of-the-art performance in generating high-affinity ligands and capturing realistic conformational changes.
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.