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Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers / Bhati, Agastya P (University Coll. London) ; Wan, Shunzhou (U. Coll. London) ; Alfè, Dario (U. Coll. London) ; Clyde, Austin R (Chicago U.) ; Bode, Mathis (RWTH Aachen U.) ; Tan, Li (Brookhaven Natl. Lab.) ; Titov, Mikhail (Rutgers U., Piscataway) ; Merzky, Andre (Rutgers U., Piscataway) ; Turilli, Matteo (Rutgers U., Piscataway) ; Jha, Shantenu (Brookhaven Natl. Lab. ; Rutgers U., Piscataway) et al.
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. [...]
2021 - 12 p. - Published in : Interface Focus 11 (2021) 20210018

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