Chapter 10 - Translational bioinformatics methods for drug discovery and drug repurposing

Abstract

Novel and in the infant stage, translational bioinformatics (TBI) may have a climactic role to ameliorate drug discovery and drug repurposing by bridging the gap between clinical biomedical data and pharmaceutical research. TBI is simply computer-related activities designed to get clinically feasible information from very large datasets for the improvement of human health. Drug discovery is the preliminary phase in the generation of newer drugs, while drug repurposing is the determination of a novel prognostic marker for an existing drug. Drug designing is widely popular amongst researchers round the globe because of its direct market applicability. TBI approaches are inclusive of an amalgamation of computational, biologic, chemical, physical, and statistical domains useful for providing powerful support for organizing and integrating analyses, data mining, disease prediction, prognostic marker identification, and drug targeting and reprofiling. TBI has the onus to meet difficulties that lie in paving a road for the development of personalized medicine in healthcare. With the advent of artificial intelligence in medicine, many potential drug leads have been identified for various diseases. Many pharmaceutical companies employ artificial intelligence for drug discovery, such as Amgen, which uses MELLODDY to train machine learning models on datasets from myriad sources for optimal drug compound for diseases. This chapter highlights the potential of the expanding TBI approaches that have paved a way for betterment in drug discovery and designing for potentially lethal diseases that exist today.