Integrating Protein Dynamics into Structure-Based Drug Design via Full-Atom Stochastic Flows
This addresses the problem of rigid protein structures in drug discovery for pharmaceutical researchers, offering an incremental improvement by combining generative modeling with molecular dynamics data.
The paper tackles the limitation of traditional structure-based drug design (SBDD) by integrating protein dynamics, proposing DynamicFlow, a generative model that transforms apo protein pockets and noisy ligands into holo states, resulting in promising ligand molecules and improved inputs for SBDD.
The dynamic nature of proteins, influenced by ligand interactions, is essential for comprehending protein function and progressing drug discovery. Traditional structure-based drug design (SBDD) approaches typically target binding sites with rigid structures, limiting their practical application in drug development. While molecular dynamics simulation can theoretically capture all the biologically relevant conformations, the transition rate is dictated by the intrinsic energy barrier between them, making the sampling process computationally expensive. To overcome the aforementioned challenges, we propose to use generative modeling for SBDD considering conformational changes of protein pockets. We curate a dataset of apo and multiple holo states of protein-ligand complexes, simulated by molecular dynamics, and propose a full-atom flow model (and a stochastic version), named DynamicFlow, that learns to transform apo pockets and noisy ligands into holo pockets and corresponding 3D ligand molecules. Our method uncovers promising ligand molecules and corresponding holo conformations of pockets. Additionally, the resultant holo-like states provide superior inputs for traditional SBDD approaches, playing a significant role in practical drug discovery.