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10.2174/1568026617666161116143440 http://scihub22266oqcxt.onion/10.2174/1568026617666161116143440 C5421137!5421137!27848897     free     free     free       Curr+Top+Med+Chem 2017 ; 17 (15): 1709-26 Nephropedia Template TP {{tp|p=27848897|t=2017. Bioinformatics and Drug Discovery |pdf=|usr=}}{{27848897}} gab.com Text Bioinformatics and Drug Discovery JO: Curr Top Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=27848897 #FOAvip 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. Twit Text FOAVip Bioinformatics and Drug Discovery ????Curr Top Med Chem http://www.kidney.de/pget.php?&tw=1&pmid=27848897 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27848897 #FOAvip English Wikipedia <ref>{{Cite pmid|27848897}}</ref> Bioinformatics and Drug Discovery #MMPMID27848897 Xia X Curr Top Med Chem 2017[Jun]; 17 (15): 1709-26 PMID27848897show ga 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. äBioinformatics and Drug Discovery (epmc).pdf DeepDyve Pubget Overpricing