Bhati, AP;
Wan, S;
Alfè, D;
Clyde, AR;
Bode, M;
Tan, L;
Titov, M;
... Coveney, PV; + view all
(2021)
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers.
Interface Focus
, 11
(6)
, Article 20210018. 10.1098/rsfs.2021.0018.
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Abstract
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow. There is a major bottleneck screening the vast number of potential small molecules to shortlist lead compounds for antiviral drug development. New opportunities to accelerate drug discovery lie at the interface between machine learning methods, in this case, developed for linear accelerators, and physics-based methods. The two in silico methods, each have their own advantages and limitations which, interestingly, complement each other. Here, we present an innovative infrastructural development that combines both approaches to accelerate drug discovery. The scale of the potential resulting workflow is such that it is dependent on supercomputing to achieve extremely high throughput. We have demonstrated the viability of this workflow for the study of inhibitors for four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead antiviral compounds through repurposing on a variety of supercomputers.
| Type: | Article |
|---|---|
| Title: | Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsfs.2021.0018 |
| Publisher version: | https://doi.org/10.1098/rsfs.2021.0018 |
| Language: | English |
| Additional information: | © 2021 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | machine learning, free energy predictions, molecular dynamics, novel drug design, artificial intelligence |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Chemistry UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Earth Sciences |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10136351 |
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