Role of Bioinformatics in Drug Design and Discovery

Abstract

Bioinformatics can speed up the identification of therapeutic targets, screening drug candidates, and refinement of those candidates. It can also make it easier to characterise side effects and anticipate drug resistance. Data from transcriptomic, proteomic, transcriptomic architecture, ribosome profiling, epigenetic, cistromic, and genomic sources, among other high-throughput data, have all contributed significantly to the development of mechanism-based drugs and pharmacological repurposing. Large-scale databases of tiny chemicals’ structures and metabolites, along with the building up of RNA and protein structures, predictions of protein structure and homology mapping, provided the ground for much accurate docking of protein and compound investigations and much insightful virtual refining. The outlined conceptual framework underpins the high-throughput data storage, summarises the value and mining potential for these data for drugs, and points out some underlying restrictions inside the data and the programs used to mine them, indicating novel approaches to improve examination of these different information formats, highlighting popular libraries, and software that are pertinent to drugs.