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. |
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| 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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