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

Nishant Jha, Deepak Prashar, Mamoon Rashid, Mohammad Shafiq, Razaullah Khan, Catalin I. Pruncu, Shams Tabrez Siddiqui, M. Saravana Kumar

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
3 Downloads (Pure)


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.]
Original languageEnglish
Article number6668985
Number of pages13
JournalJournal of Healthcare Engineering
Publication statusPublished - 20 Jul 2021


  • SARS-CoV-2 - drug effects
  • COVID-19 - drug therapy
  • drug discovery - methods
  • algorithms
  • pharmaceutical preparations
  • computer simulation
  • pandemics
  • humans
  • deep learning


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