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.

Raghavan, B., Paulikat, M., Ahmad, K., Callea, L., Rizzi, A., Ippoliti, E., et al. (2023). Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 63(12 (26 June 2023)), 3647-3658 [10.1021/acs.jcim.3c00557].

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

Callea L.;Bonati L.;
2023

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.
Articolo in rivista - Articolo scientifico
MiMiC, QM/MM MD, exascale, drug design, ligand binding
English
15-giu-2023
2023
63
12 (26 June 2023)
3647
3658
none
Raghavan, B., Paulikat, M., Ahmad, K., Callea, L., Rizzi, A., Ippoliti, E., et al. (2023). Drug Design in the Exascale Era: A Perspective from Massively Parallel QM/MM Simulations. JOURNAL OF CHEMICAL INFORMATION AND MODELING, 63(12 (26 June 2023)), 3647-3658 [10.1021/acs.jcim.3c00557].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/439138
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