Machine Learning Techniques for Development of Drugs Against Coronavirus Disease 2019 (COVID-19): A Case Study Protocol

Abstract

Discovery of SARS-CoV-2 drug requires a fast track approach to achieve the effective and safe alternative. The present times call for an evolution in the process of drug discovery. Several factors contribute to high cost and long development times associated with new drugs. Finding a new molecular target is contingent upon a detailed understanding of the disease pathology, which often takes years of basic research. Integrating genetic and expression studies with Protein Interaction Network (PIN), and considering both functional and topological features of the resultant network may prove to be an effective target identification strategy. Further, apart from the existing computational tools to identify ligands, artificial intelligence approaches may now be used to increase the search space many folds, offering a faster method for screening. Artificial intelligence can be integrated with the existing drug discovery pipeline to enable rational target identification, prediction of an accurate 3D structure of the molecular target and screen large ligand libraries for putative modulators. The present chapter covers a detailed protocol to scan and validate the therapeutic targets for COVID-19, and screen the compounds for future in vitro or in vivo validation. The chapter covers target selection strategies, and application of artificial intelligence to identify drug–target interactions.