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Molecular Dynamics and Other HPC Simulations for Drug Discovery

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High Performance Computing for Drug Discovery and Biomedicine

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2716))

Abstract

High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus hel** the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.

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Abbreviations

A1AR:

A1 Adenosine Receptor

ACE2:

Angiotensin-Converting Enzyme 2

AF:

AlphaFold

AI:

Artificial Intelligence

AMBER:

Assisted Model Building with Energy Refinement

CADD:

Computer-Aided Drug Design

CAPRI:

Critical Assessment of Prediction of Interactions

CASP14:

Critical Assessment of Structure Prediction, round 14

CGenFF:

Charmm General Force Field

CHARMM:

Chemistry at HARvard Molecular Mechanics

COVID-19:

COrona VIrus Disease 2019

CPU:

Central Processing Unit

Cryo-EM:

Cryo-Electron Microscopy

DMTA:

Design, Make, Test, Analyze

DNA:

Deoxyribonucleic Acid

DNN:

Deep Neural Network

DUD-E:

Database of Useful Decoys Enhanced

ECL2:

Extra Cellular Loop 2

FEP:

Free Energy Perturbation

FF:

Force Field

FGFR2:

Fibroblast Growth Factor Receptor 2

GAFF:

Generalized Amber Force Field

GAMD:

Gaussian Accelerated Molecular Dynamics

GPCR:

G-Protein Coupled Receptor

GPU:

Graphical Processing Unit

GROMACS:

GROningen Machine for Chemical Simulations

H2L:

Hit to Lead

HPC:

High-Performance Computing

IC50:

Half Maximal Inhibitory Concentration

IL2:

InterLeukin 2

JAK1:

Janus Kinase 1

JAK2:

Janus Kinase 2

LO:

Lead Optimization

MC:

Monte Carlo

MD:

Molecular Dynamics

MixMD:

Mixed-Solvent Molecular Dynamics

MMFF:

Merck Molecular Force Field

MM-GBSA:

Molecular Mechanics Generalized Born Surface Area

MM-PBSA:

Molecular Mechanics Poisson-Boltzmann Surface Area

MSA:

Multiple Sequence Alignment

MSM:

Markov State Model

NAMD:

Nanoscale Molecular Dynamics

NCATS:

National Center for Advancing Translational Sciences

ns:

nano second

OPLS4:

Optimized Potentials for Liquid Simulation, Version 4

PAM:

Positive Allosteric Modulator

PDB:

Protein Data Bank

PELE:

Protein Energy Landscape Exploration

PFLOP:

Peta FLoating-point Operations Per second

PMF:

Potential of Mean Force

PPI:

Protein-Protein Interactions

PRC:

Pose Ranking Consensus

QCP:

Quaternion-Based Characteristic Polynomial

QM:

Quantum Mechanics

QSAR:

Quantitative Structure Activity Relationship

RBD:

Receptor Binding Domain

RdRp:

RNA-Dependent RNA polymerase

RMSD:

Root Mean Square Deviation

RNA:

Ribonucleic Acid

SARS-CoV-2:

Severe Acute Respiratory Syndrome CoronaVirus 2

SAXS:

Small-Angle X-Ray Scattering

SBVS:

Structure-Based Virtual Screening

TPU:

Tensor Processing Unit

TREMD:

Temperature Replica Exchange Molecular Dynamics

VS:

Virtual Screening

μOR:

μ-Opioid Receptor

μs:

micro second

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Acknowledgments

The authors thank Brice Sautier and Gaurao Dhoke (Evotec (France) SAS, Toulouse, France) for valuable suggestions to improve the manuscript.

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  1. Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France

    Martin Kotev & Constantino Diaz Gonzalez

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  1. Martin Kotev
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  2. Constantino Diaz Gonzalez
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Correspondence to Constantino Diaz Gonzalez .

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  1. In Silico Research and Development, Evotec UK Ltd, Abingdon, UK

    Alexander Heifetz

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Kotev, M., Diaz Gonzalez, C. (2024). Molecular Dynamics and Other HPC Simulations for Drug Discovery. In: Heifetz, A. (eds) High Performance Computing for Drug Discovery and Biomedicine. Methods in Molecular Biology, vol 2716. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3449-3_12

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  • DOI: https://doi.org/10.1007/978-1-0716-3449-3_12

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