View in EDS

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

Saved in:
Bibliographic Details
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
Description
ISSN:1549-960X
DOI:10.1021/acs.jcim.7b00475