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

TL;DR

Apo2Mol introduces a diffusion model that generates 3D ligands and protein pocket conformations simultaneously, explicitly modeling protein flexibility to improve structure-based drug design.

Contribution

It is the first to incorporate conformational flexibility of protein pockets into 3D molecule generation using a diffusion framework.

Findings

Achieves state-of-the-art ligand affinity prediction.

Accurately models protein pocket conformational changes.

Generates realistic 3D ligand and pocket structures.

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.