Deep learning approach for discovery of in silico drugs for combating COVID-19

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Jha, Nishant and Prashar, Deepak and Rashid, Mamoon and Shafiq, Mohammad and Khan, Razaullah and Pruncu, Catalin I. and Tabrez Siddiqui, Shams and Saravana Kumar, M. (2021) Deep learning approach for discovery of in silico drugs for combating COVID-19. Journal of Healthcare Engineering, 2021. 6668985. ISSN 2040-2295 (https://doi.org/10.1155/2021/6668985)

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Abstract

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19. [Abstract copyright: Copyright © 2021 Nishant Jha et al.]

ORCID iDs

Jha, Nishant, Prashar, Deepak, Rashid, Mamoon, Shafiq, Mohammad, Khan, Razaullah, Pruncu, Catalin I. ORCID logoORCID: https://orcid.org/0000-0002-4926-2189, Tabrez Siddiqui, Shams and Saravana Kumar, M.;
CORE (COnnecting REpositories)