A Diffusion-Based Framework for Designing Molecules in Flexible Protein Pockets

Abstract

The design of molecules for flexible protein pockets represents a significant challenge in structure-based drug discovery, as proteins often undergo conformational changes upon ligand binding. While deep learning-based approaches have shown promise in molecular generation, they typically treat protein pockets as rigid structures, limiting their ability to capture the dynamic nature of protein-ligand interactions. Here, we introduce YuelDesign, a novel diffusion-based framework specifically developed to address this challenge. YuelDesign employs a new protein encoding scheme with a fully connected graph representation to encode protein pocket flexibility, a systematic denoising process that refines both atomic properties and coordinates, and a specialized bond reconstruction module tailored for de novo generated molecules. Our results demonstrate that YuelDesign generates molecules with favorable drug-likeness and low synthetic complexity. The generated molecules also exhibit diverse chemical functional groups, including some not even present in the training set. Redocking analysis reveals that the generated molecules exhibit docking energies comparable to native ligands. Additionally, a detailed analysis of the denoising process shows how the model systematically refines molecular structures through atom type transitions, bond dynamics, and conformational adjustments. Overall, YuelDesign presents a versatile framework for generating novel molecules tailored to flexible protein pockets, with promising implications for drug discovery applications.