Elsevier

Drug Discovery Today

Volume 17, Issues 19–20, October 2012, Pages 1059-1062
Drug Discovery Today

Perspective
Feature
High-throughput molecular dynamics: the powerful new tool for drug discovery

https://doi.org/10.1016/j.drudis.2012.03.017Get rights and content

Molecular dynamics simulations are capable of resolving molecular recognition processes with chemical accuracy, but their practical application is popularly considered limited to the timescale accessible to a single simulation, which is far below biological timescales. In this perspective article, we propose that the true limiting factor for molecular dynamics is rather the high hardware and electrical power costs, which constrain not only the length of runs but also the number that can be performed concurrently. As a result of innovation in accelerator processors and high-throughput protocols, the cost of molecular dynamics sampling has been dramatically reduced and we argue that molecular dynamics simulation is now placed to become a key technology for in silico drug discovery in terms of binding pathways, poses, kinetics and affinities.

Highlights

► Molecular dynamics (MD) is able to provide quantitative insight into the kinetics of drug-ligand binding processes. ► MD cost substantially reduced by new developments in computing hardware. ► Markov state modelling using moves MD simulation from a high performance to high-throughput activity. ► High-throughput MD now a valuable tool in computational drug design.

Section snippets

Technology of MD simulation

MD modelling of biomolecules typically treats each atom of the solvent and solute as separate point particle. A force-field, parametrised to capture the chemical properties of the environment of each type of particle governs the evolution of the system, which proceeds according to Newtonian dynamics in a stepwise manner. At each step of the simulation, the net force on each particle is calculated and the particles’ positions updated.

To correctly capture the dynamics of the system the timestep

Exploiting high-throughput MD

The desire to access long timescales within a reasonable time has led to the development of a variety of enhanced sampling techniques, which typically impose constraints or biases to accelerate the evolution of a system. These methods require some a priori knowledge about the system so that a reaction coordinate or order parameter can be defined to guide the enhanced sampling. The evolution of these techniques has been motivated by the need to extract the maximum sampling from single long

Concluding remarks

In this perspective we argue that the reduction in cost of GPUs, together with high-throughput MD protocols can provide a way to resolve the physical chemistry of molecular recognition, for instance using reasonably sized GPU clusters and high-throughput protocols. MD simulation should move from high performance computing, with its focus on maximising length of a small number of individual simulations, to high-throughput, where large ensembles of independent, ‘long enough’ simulations may be

Conflict of interest

The authors declare a financial interest in Acellera Ltd.

Acknowledgement

The authors thank David Soriano for a critical reading of the manuscript.

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