Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers

• URL: http://arxiv.org/abs/2103.02843v1
• Date: Thu, 4 Mar 2021 05:43:18 GMT
• Title: Pandemic Drugs at Pandemic Speed: Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
• Authors: Agastya P. Bhati, Shunzhou Wan, Dario Alf\`e, Austin R. Clyde, Mathis Bode, Li Tan, Mikhail Titov, Andre Merzky, Matteo Turilli, Shantenu Jha, Roger R. Highfield, Walter Rocchia, Nicola Scafuri, Sauro Succi, Dieter Kranzlm\"uller, Gerald Mathias, David Wifling, Yann Donon, Alberto Di Meglio, Sofia Vallecorsa, Heng Ma, Anda Trifan, Arvind Ramanathan, Tom Brettin, Alexander Partin, Fangfang Xia, Xiaotan Duan, Rick Stevens, Peter V. Coveney
• Abstract summary: Existing drug discovery process is expensive, inefficient and slow. New opportunities to accelerate drug discovery lie at the interface between machine learning methods and physics-based methods. Here, we present an innovative method that combines both approaches to accelerate drug discovery.
• Score: 36.11665744919561
• License: http://creativecommons.org/licenses/by/4.0/
• 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 method that combines both approaches to accelerate drug discovery. The scale of the resulting workflow is such that it is dependent on high performance computing. We have demonstrated the applicability of this workflow on four COVID-19 target proteins and our ability to perform the required large-scale calculations to identify lead compounds on a variety of supercomputers.

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