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A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.

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Title: A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.
Authors: Kaiser TM; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States., Burger PB; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.; Department of Drug Discovery and Biomedical Sciences, College of Pharmacy , Medical University of South Carolina , 280 Calhoun St., MSC 141 , Charleston , South Carolina 29425-1410 , United States., Butch CJ; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.; Earth-Life Science Institute , Tokyo Institute of Technology , 2-12-1-IE-1 Ookayam , Meguro-ku , Tokyo 152-8550 , Japan., Pelly SC; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States., Liotta DC; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.
Source: Journal of chemical information and modeling [J Chem Inf Model] 2018 Aug 27; Vol. 58 (8), pp. 1544-1552. Date of Electronic Publication: 2018 Jul 17.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: American Chemical Society Country of Publication: United States NLM ID: 101230060 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1549-960X (Electronic) Linking ISSN: 15499596 NLM ISO Abbreviation: J Chem Inf Model Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.
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  Data: <searchLink fieldCode="AU" term="%22Kaiser+TM%22">Kaiser TM</searchLink>; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.<br /><searchLink fieldCode="AU" term="%22Burger+PB%22">Burger PB</searchLink>; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.; Department of Drug Discovery and Biomedical Sciences, College of Pharmacy , Medical University of South Carolina , 280 Calhoun St., MSC 141 , Charleston , South Carolina 29425-1410 , United States.<br /><searchLink fieldCode="AU" term="%22Butch+CJ%22">Butch CJ</searchLink>; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.; Earth-Life Science Institute , Tokyo Institute of Technology , 2-12-1-IE-1 Ookayam , Meguro-ku , Tokyo 152-8550 , Japan.<br /><searchLink fieldCode="AU" term="%22Pelly+SC%22">Pelly SC</searchLink>; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.<br /><searchLink fieldCode="AU" term="%22Liotta+DC%22">Liotta DC</searchLink>; Department of Chemistry , Emory University , 201 Dowman Drive , Atlanta , Georgia 30322 , United States.
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  Data: <searchLink fieldCode="JN" term="%22101230060%22">Journal of chemical information and modeling</searchLink> [J Chem Inf Model] 2018 Aug 27; Vol. 58 (8), pp. 1544-1552. <i>Date of Electronic Publication: </i>2018 Jul 17.
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        Value: 10.1021/acs.jcim.7b00475
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      – Code: eng
        Text: English
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      – TitleFull: A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds.
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            – D: 27
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              Text: 2018 Aug 27
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