Abstract
The recent COVID-19 pandemic has served as a timely reminder that the existing drug discovery is a laborious, expensive, and slow process. Never has there been such global demand for a therapeutic treatment to be identified as a matter of such urgency. Unfortunately, this is a scenario likely to repeat itself in future, so it is of interest to explore ways in which to accelerate drug discovery at pandemic speed. Computational methods naturally lend themselves to this because they can be performed rapidly if sufficient computational resources are available. Recently, high-performance computing (HPC) technologies have led to remarkable achievements in computational drug discovery and yielded a series of new platforms, algorithms, and workflows. The application of artificial intelligence (AI) and machine learning (ML) approaches is also a promising and relatively new avenue to revolutionize the drug design process and therefore reduce costs. In this review, I describe how molecular dynamics simulations (MD) were successfully integrated with ML and adapted to HPC to form a powerful tool to study inhibitors for four of the COVID-19 target proteins. The emphasis of this review is on the strategy that was used with an explanation of each of the steps in the accelerated drug discovery workflow. For specific technical details, the reader is directed to the relevant research publications.
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Acknowledgements
Corresponding author is grateful to Profs Peter Coveney and Andrea Townsend-Nicholson from University College London for their support, to the European Commission for the EU H2020 CompBioMed2 Centre of Excellence (grant no. 823712), and to Dr. Tim Holt, senior publishing editor of Interface Focus that allowed the author to describe the findings originally published in Interface Focus [5] as a source of information for the current review.
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Heifetz, A. (2024). Accelerating COVID-19 Drug Discovery with High-Performance Computing. 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_19
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DOI: https://doi.org/10.1007/978-1-0716-3449-3_19
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