The initial phases of drug discovery –in silicodrug 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.