Deep Learning-Based Drug Screening for COVID-19 and Case Studies

ABSTRACT

Coronavirus infectious disease (COVID-19), caused by deadly severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared as a pandemic by the World Health Organization. This disease has become the world’s worst infectious disease, killing about 1.05 million lives as of September 2020. The absence of vaccines and effective drugs is a key trouble responsible for the ineffective management of this pandemic. Considering this emergency situation, several trials have been made to identify repurposing on-market drugs known for their antiviral behavior. Several modern technologies such as deep machine learning are used to combat this deadly disease with faster prediction and greater accuracy. More interestingly, these studies have provided clues for the antiviral properties and are believed to help in effective control of this pandemic. The drugs identified by deep learning-based virtual screening will help in unraveling molecular mechanisms of therapeutic and antiviral properties and will pave the way for designing artificial drugs. Hence we focus in this chapter on the integrated applications of deep learning models as a pipeline for drug and vaccine discovery which has implications in therapeutic drug targeting for COVID-19.