Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations

The initial phases of drug discovery - in silico drug design - could benefit from firstprinciple QuantumMechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulationsin explicit solvent, yet many applications are currently limited bythe short time scales that this approach can cover. Developing scalablefirst principle QM/MM MD interfaces fully exploiting current exascalemachines - so far an unmet and crucial goal - will helpovercome this problem, opening the way to the study of the thermodynamicsand kinetics of ligand binding to protein with first principle accuracy.Here, taking two relevant case studies involving the interactionsof ligands with rather large enzymes, we showcase the use of our recentlydeveloped massively scalable Multiscale Modeling in ComputationalChemistry (MiMiC) QM/MM framework (currently using DFT to describethe QM region) to investigate reactions and ligand binding in enzymesof pharmacological relevance. We also demonstrate for the first timestrong scaling of MiMiC-QM/MM MD simulations with parallel efficiencyof & SIM;70% up to >80,000 cores. Thus, among many others, theMiMiCinterface represents a promising candidate toward exascale applicationsby combining machine learning with statistical mechanics based algorithmstailored for exascale supercomputers.

Automated summary

Currently, in silico drug design in the initial stages of drug discovery can benefit from first-principle Quantum Mechanics/Molecular Mechanics (QM/MM) molecular dynamics (MD) simulations in explicit solvent, but many applications are limited by the short time scales that this approach can cover. The development of scalable first principle QM/MM MD interfaces that fully exploit current exascale machines is crucial in overcoming this problem and allowing for the study of ligand binding to protein with first principle accuracy. This study showcases the use of the Multiscale Modeling in Computational Chemistry (MiMiC) QM/MM framework, which demonstrates strong scaling and parallel efficiency of >70% up to >80,000 cores, making it a promising candidate for exascale applications.