Bioinformatics and Drug Discovery

Title:Bioinformatics and Drug Discovery

Volume: 17 Issue: 15

Author(s): Xuhua Xia*

Affiliation:

• Department of Biology, Faculty of Science, University of Ottawa, Ottawa, Ontario,Canada

Keywords: Drug target, Drug candidate, Drug screening, Genomics, Epigenetics, Transcriptomics, Proteomics, Structure.

Abstract: Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

Cite this article as:

Xia Xuhua*, Bioinformatics and Drug Discovery, Current Topics in Medicinal Chemistry 2017; 17 (15) . https://dx.doi.org/10.2174/1568026617666161116143440