Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers

@article{Bhati2021PandemicDA,
  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},
  author={Agastya P. Bhati and Shunzhou Wan and Dario Alf{\`e} and Austin R. Clyde and Mathis Bode and Li Tan and Mikhail Titov and Andr{\'e} Merzky and Matteo Turilli and Shantenu Jha and R. Highfield and Walter Rocchia and Nicola Scafuri and Sauro Succi and Dieter August Kranzlm{\"u}ller and Gerald Mathias and David Wifling and Yann Donon and Alberto Di Meglio and Sofia Vallecorsa and Heng Ma and Anda Trifan and Arvind Ramanathan and Thomas S. Brettin and Alexander Partin and Fangfang Xia and Xiaotan Duan and Rick L. Stevens and Peter V. Coveney},
  journal={Interface Focus},
  year={2021},
  volume={11}
}
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… 

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