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Computational Intelligence in Drug Repurposing for COVID-19

Part of the Studies in Computational Intelligence book series (SCI,volume 923)

Abstract

Severe acute respiratory syndrome is a viral respiratory infection known as COVID-19, which is caused by a novel coronavirus, called SARS-associated coronavirus-2 (SARS-CoV-2). Considering it as an international concern, WHO declared COVID-19 as the “sixth public health emergency” and has termed it as ‘pandemic”. Currently, no specific drugs are available, and studies about COVID-19 treatment are still in progress. As the world is facing a major challenge in trying to adapt and defend itself against this new pandemic disease, computational intelligence offers a new hope that a cure to this disease might be developed faster than ever before. Many targets for the design of drugs have been already identified, and studies are in progress to explore these potential targets. Computational approaches like virtual screening, molecular docking, machine learning, deep learning and natural language processing (NLP) play a vital role in drug repurposing studies. Repurposing drugs involves discovering novel drug-target interactions and their use against the treatment of different diseases. This strategy has regained significant interest to develop a drug against the COVID-19 considering this pandemic scenario, and offers the best chance to identify potent drugs from the list of approved drugs. Various research efforts are currently focusing on the identification of existing drugs which might be useful in mitigating the infection and some compounds namely favipiravir, remdesivir, lopinavir, hydroxychloroquine etc. are in the final stage of human testing.

Keywords

  • COVID-19
  • Computational intelligence
  • Drug repurposing
  • Molecular modelling
  • Drug designing

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Tripathi, M.K., Sharma, S., Singh, T.P., Ethayathulla, A.S., Kaur, P. (2021). Computational Intelligence in Drug Repurposing for COVID-19. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_14

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