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10.3390/ph11020057 http://scihub22266oqcxt.onion/10.3390/ph11020057 C6027196!6027196 !29874824     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=29874824 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\29874824 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Pharmaceuticals+(Basel) 2018 ; 11 (2 ): ä Nephropedia Template TP {{tp|p=29874824 |t=2018. Changing Trends in Computational Drug Repositioning |pdf=|usr=}}{{29874824 }} gab.com Text Changing Trends in Computational Drug Repositioning JO: Pharmaceuticals (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=29874824 #FOAvip Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning. Twit Text FOAVip Changing Trends in Computational Drug Repositioning ????Pharmaceuticals (Basel) http://www.kidney.de/pget.php?&tw=1&pmid=29874824 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29874824 #FOAvip English Wikipedia <ref>{{Cite pmid|29874824 }}</ref> Changing Trends in Computational Drug Repositioning #MMPMID29874824 Yella JK ; Yaddanapudi S ; Wang Y ; Jegga AG Pharmaceuticals (Basel) 2018[Jun]; 11 (2 ): ä PMID29874824 show ga Efforts to maximize the indications potential and revenue from drugs that are already marketed are largely motivated by what Sir James Black, a Nobel Prize-winning pharmacologist advocated-"The most fruitful basis for the discovery of a new drug is to start with an old drug". However, rational design of drug mixtures poses formidable challenges because of the lack of or limited information about in vivo cell regulation, mechanisms of genetic pathway activation, and in vivo pathway interactions. Hence, most of the successfully repositioned drugs are the result of "serendipity", discovered during late phase clinical studies of unexpected but beneficial findings. The connections between drug candidates and their potential adverse drug reactions or new applications are often difficult to foresee because the underlying mechanism associating them is largely unknown, complex, or dispersed and buried in silos of information. Discovery of such multi-domain pharmacomodules-pharmacologically relevant sub-networks of biomolecules and/or pathways-from collection of databases by independent/simultaneous mining of multiple datasets is an active area of research. Here, while presenting some of the promising bioinformatics approaches and pipelines, we summarize and discuss the current and evolving landscape of computational drug repositioning. äChanging Trends in Computational Drug Repositioning (epmc).pdf DeepDyve Pubget Overpricing