15 Years of molecular simulation of drug-binding kinetics

ABSTRACT

Introduction

Drug-binding kinetics has been increasingly recognized as an important factor to be considered in drug discovery. Long residence time could prolong the action of some drugs while produce toxicity on others. Early evaluation of the binding kinetics of drug candidates could reduce attrition rate late in the drug discovery process. Computational prediction of drug-binding kinetics is useful as compounds can be evaluated even before they are made. However, simulation of drug-binding kinetics is a challenging problem because of the long-time scale involved. Nevertheless, significant progress has been made.

Areas covered

This review illustrates the rapid evolution of qualitative to quantitative molecular dynamics-based methods that have been developed over the last 15 years.

Expert opinion

The development of new methods based on molecular dynamics simulations now enables computation of absolute association/dissociation rate constants. Cheaper methods capable of identifying candidates with fast or slow binding kinetics, or rank-ordering rate constants are also available. Together, these methods have generated useful insights into the molecular mechanisms of drug-binding kinetics, and the design of drug candidates with therapeutically favorable kinetics. Although predicting absolute rate constants is still expensive and challenging, rapid improvement is expected in the coming years with the continuing refinement of current technologies, development of new methodologies, and the utilization of machine learning.

KEYWORDS:

• Drug-binding kinetics
• Markov State Model
• metadynamics simulation
• milestoning simulation
• scaled, steered, or random accelerated molecular dynamics
• umbrella-sampling simulation
• weighted ensemble simulation
• machine learning

Article highlights

• Drug-binding kinetics has become an important factor for consideration in drug discovery.

• Molecular dynamics-based simulations have helped to decipher the molecular mechanisms of drug-binding kinetics and design compounds with therapeutically useful kinetic parameters.

• The last 15 years have seen significant progress in moving from qualitative to quantitative models. This review examines how a subset of methods have been used to study drug-binding kinetics. These methods include a mining-minima approach, steered molecular dynamics, τ-random accelerated molecular dynamics, scaled molecular dynamics, umbrella sampling simulation, Markov State Model, milestoning simulation, weighted ensemble simulation, and metadynamics simulation. Some of these methods allow absolute, not only relative, association/dissociation rates to be computed.

• Machine learning has been used with molecular dynamics to improve the study of drug-binding kinetics.

• Because it is still expensive to compute absolute association/dissociation rates, the number of systems studied is still small. However, the increase in the number of research groups tackling this problem should help to validate methodologies at a more rapid pace.

Acknowledgments

The author thanks Cynthia Jobe for her assistance with English editing.

Declaration of interest

The author has no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

This author is supported by the US National Institutes of Health via grant [CA224033].