A Machine Learning Approach for Predicting HIV Reverse Transcriptase Mutation Susceptibility of Biologically Active Compounds-Reference-Cited by-同舟云学术

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

Published:2018-06-28 Issue:8 Volume:58 Page:1544-1552
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Kaiser Thomas M.¹^ORCID,Burger Pieter B.¹²,Butch Christopher J.¹³,Pelly Stephen C.¹,Liotta Dennis C.¹^ORCID

Affiliation:

1. Department of Chemistry, Emory University, 201 Dowman Drive, Atlanta, Georgia 30322, United States

2. 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

3. Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1-IE-1 Ookayam, Meguro-ku, Tokyo 152-8550, Japan

Funder

John Templeton Foundation

Emory University

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.7b00475

Reference23 articles.

1. The successes and failures of HIV drug discovery

2. Adherence to antiretroviral HIV drugs: how many doses can you miss before resistance emerges?

3. Medication adherence in HIV-infected adults

4. Fidelity of HIV-1 reverse transcriptase copying RNA in vitro

5. Design and Validation of FRESH, a Drug Discovery Paradigm Resting on Robust Chemical Synthesis

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