AI based strategies for Covid-19 drug repurposing.
Sonakshi Srivastava, P. Preeti, Trapti Sharma, Shruti Goel, Kanchan Gulabsing Rajput, Kamal Rawal · 2022
Background:The novel coronavirus disease, COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused catastrophic effects resulting in over 5.6 million deaths worldwide as of February 2022 and approximately 3.6 million new cases have resulted. The traditional drug discovery methodology is a risky, lengthy and expensive process, and due to the urgency to discover new therapies and treatments, the drug repositioning strategy has been driven by its potential to identify compounds that could be used to treat the symptoms. Viral infection attracts attention.Method:It was discovered that the convolutional neural network (CNN) and its modified models were mainly used for COVID-19 pandemic prediction, whereas in the case of machine learning (ML), the support vector machine (SVM), and random forest (RF) was largely utilized for COVID-19 pandemic combat.Result:In the case of COVID-19, modern technologies such as AI and ML have been used effectively to identify remdesivir alongside other drugs to treat COVID-19. It has shown promise in treating COVID-19, prompting the FDA to issue emergency use authorization, although it is only limited to severe conditions. The FDA made this decision based on early research showing the drug could help speed recovery in hospitalized patients with COVID-19.