Chapter 9 - Computational demonstration of cheminformatics and machine learning in coronavirus drug discovery

Abstract

Computational tools in drug discovery involve the use of algorithms in predicting properties of potential drugs as ligands as well as biological targets in structural forms. This dates back to more than 30 years ago and have been perfected with time and advancement of technology. They are reliable to varying extents depending on the nature of the study, complexity among other factors. Computational tools help medicinal chemists, computational chemists, and structural biologists to design and optimize potential drugs as early as possible and reduce or completely avoid attrition in the drug discovery pipeline. The search for drugs to cure or manage COVID-19 is made relatively easier and more efficient by the use of computational tools to help understand the ADMET properties of possible drugs under development. This chapter demonstrates how computational tools in cheminformatics and machine learning can be used in the fight against COVID-19 from a medicinal chemistry perspective using selected parameters.