HemePLM-Diffuse: A Scalable Generative Framework for Protein-Ligand Dynamics in Large Biomolecular System
This addresses a computationally intensive problem in drug discovery and structural biology, offering a scalable solution for large biomolecular systems.
The paper tackled the challenge of simulating long-timescale dynamics in large protein-ligand complexes for drug discovery by introducing HemePLM-Diffuse, a generative transformer model that achieved enhanced accuracy and scalability on systems with over 10,000 atoms compared to leading models like TorchMD-Net, MDGEN, and Uni-Mol.
Comprehending the long-timescale dynamics of protein-ligand complexes is very important for drug discovery and structural biology, but it continues to be computationally challenging for large biomolecular systems. We introduce HemePLM-Diffuse, an innovative generative transformer model that is designed for accurate simulation of protein-ligand trajectories, inpaints the missing ligand fragments, and sample transition paths in systems with more than 10,000 atoms. HemePLM-Diffuse has features of SE(3)-Invariant tokenization approach for proteins and ligands, that utilizes time-aware cross-attentional diffusion to effectively capture atomic motion. We also demonstrate its capabilities using the 3CQV HEME system, showing enhanced accuracy and scalability compared to leading models such as TorchMD-Net, MDGEN, and Uni-Mol.