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Bioinformatics and Drug Discovery

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, p...

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Detalles Bibliográficos
Autor principal: Xia, Xuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Bentham Science Publishers 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421137/
https://www.ncbi.nlm.nih.gov/pubmed/27848897
http://dx.doi.org/10.2174/1568026617666161116143440
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author Xia, Xuhua
author_facet Xia, Xuhua
author_sort Xia, Xuhua
collection PubMed
description 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 mechanism-based 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.
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spelling pubmed-54211372017-05-31 Bioinformatics and Drug Discovery Xia, Xuhua Curr Top Med Chem Article 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 mechanism-based 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. Bentham Science Publishers 2017-06 2017-06 /pmc/articles/PMC5421137/ /pubmed/27848897 http://dx.doi.org/10.2174/1568026617666161116143440 Text en © 2017 Bentham Science Publishers https://creativecommons.org/licenses/by-nc/4.0/legalcode This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
spellingShingle Article
Xia, Xuhua
Bioinformatics and Drug Discovery
title Bioinformatics and Drug Discovery
title_full Bioinformatics and Drug Discovery
title_fullStr Bioinformatics and Drug Discovery
title_full_unstemmed Bioinformatics and Drug Discovery
title_short Bioinformatics and Drug Discovery
title_sort bioinformatics and drug discovery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5421137/
https://www.ncbi.nlm.nih.gov/pubmed/27848897
http://dx.doi.org/10.2174/1568026617666161116143440
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