Corpus ID: 227254190

Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning

@article{Skwark2020DesigningAP,
  title={Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning},
  author={Marcin J. Skwark and Nicol'as L'opez Carranza and Thomas Pierrot and J. Phillips and Slim Said and Alexandre Laterre and Amine Kerkeni and Uugur cSahin and Karim Beguir},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.01736}
}

Marcin J. Skwark, Nicol'as L'opez Carranza, +6 authors Karim Beguir

Published 2020

Computer Science, Biology

ArXiv

The SARS-CoV-2 pandemic has created a global race for a cure. One approach focuses on designing a novel variant of the human angiotensin-converting enzyme 2 (ACE2) that binds more tightly to the SARS-CoV-2 spike protein and diverts it from human cells. Here we formulate a novel protein design framework as a reinforcement learning problem. We generate new designs efficiently through the combination of a fast, biologically-grounded reward function and sequential action-space formulation. The use… CONTINUE READING

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