DESIGNING POTENTIAL DRUGS THAT CAN TARGET SARS-COV-2'S MAIN PROTEASE: A PROACTIVE DEEP TRANSFER LEARNING APPROACH USING LSTM ARCHITECTUR

Authors

  • Omar Dasser, PhD student Research Laboratory Mathematics, Computer Science and Engineering Sciences, Hassan I University- Settat 26002 –
  • Moad Tahri, PhD student Laboratory of informatics research and innovation, Hassan II University, Casablanca, Morocco.
  • Louay kila, MD M.B.B.S, ECFMG Certified, Al Ain Hospital- Health Authority Abu Dhabi.
  • Abderrahim Sekkaki, PhD Laboratory of informatics research and innovation, Hassan II University, Casablanca, Morocco.

Keywords:

considered, around, average, Drug

Abstract

On December 2019, the world entered a state of alarm and dismay with the outbreak of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV 2) from Hubei-China and has infected as of the 1st October 2021 more than 233,770,079 people worldwide. This caused up to 4,782,608 deaths, and the World Health Organization (WHO) declared on January 2020 a global health emergency due to the rate at how much the infection is spreading and the mortality rate that approaches 4.5 percent [1]. It is considered to be extremely costly to bring a new drug to the market in terms of time and financial investment, which is, respectively, on average, around ten years and 1 billion dollars. Drug discovery alone can take up to 3 years which is a time we cannot accept in the context of a global pandemic

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Published

2022-07-06

How to Cite

Omar Dasser, PhD student, Moad Tahri, PhD student, Louay kila, MD, & Abderrahim Sekkaki, PhD. (2022). DESIGNING POTENTIAL DRUGS THAT CAN TARGET SARS-COV-2’S MAIN PROTEASE: A PROACTIVE DEEP TRANSFER LEARNING APPROACH USING LSTM ARCHITECTUR. World Bulletin of Public Health, 12, 1-10. Retrieved from https://scholarexpress.net/index.php/wbph/article/view/1114

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