Abstract
The initial phases of drug discovery - in silico drug design - could benefit from first principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, yet many applications are currently limited by the short time scales that this approach can cover. Developing scalable first principle QM/MM MD interfaces fully exploiting current exascale machines - so far an unmet and crucial goal - will help overcome this problem, opening the way to the study of the thermodynamics and kinetics of ligand binding to protein with first principle accuracy. Here, taking two relevant case studies involving the interactions of ligands with rather large enzymes, we showcase the use of our recently developed massively scalable Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework (currently using DFT to describe the QM region) to investigate reactions and ligand binding in enzymes of pharmacological relevance. We also demonstrate for the first time strong scaling of MiMiC-QM/MM MD simulations with parallel efficiency of ∼70% up to >80,000 cores. Thus, among many others, the MiMiC interface represents a promising candidate toward exascale applications by combining machine learning with statistical mechanics based algorithms tailored for exascale supercomputers.
| Original language | English |
|---|---|
| Pages (from-to) | 3647-3658 |
| Number of pages | 12 |
| Journal | Journal of Chemical Information and Modeling |
| Volume | 63 |
| Issue number | 12 |
| DOIs | |
| State | Published - Jun 26 2023 |
| Externally published | Yes |
Funding
Discussions with Maria João Ramos are gratefully acknowledged. AR, BR, DM, MDV, and PC thank the Helmholtz European Partnering program (“Innovative high-performance computing approaches for molecular neuromedicine”) for funding. PC and MP thank the Human Brain Project (EU Horizon 2020) for funding. The authors gratefully acknowledge the Gauss Centre for Supercomputing e.V. ( www.gauss-centre.eu ) for funding this project by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer JUWELS at the Jülich Supercomputing Centre (JSC). The support team at JSC is also acknowledged for their help in running full system simulations on the large reservation at JUWELS. Open access publication fee funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 491111487. BR, EI, MDV, and PC also acknowledge RWTH Aachen University for providing computer resources under project rwth0596. BR acknowledges useful discussions with Nitin Malapally.