Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach-Reference-Cited by-同舟云学术

Toward Reducing hERG Affinities for DAT Inhibitors with a Combined Machine Learning and Molecular Modeling Approach

Published:2021-08-21 Issue:9 Volume:61 Page:4266-4279
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Lee Kuo Hao¹,Fant Andrew D.¹,Guo Jiqing²,Guan Andy¹,Jung Joslyn¹,Kudaibergenova Mary³,Miranda Williams E.³,Ku Therese⁴,Cao Jianjing⁴,Wacker Soren²³⁵^ORCID,Duff Henry J.²,Newman Amy Hauck⁴^ORCID,Noskov Sergei Y.³^ORCID,Shi Lei¹^ORCID

Affiliation:

1. Computational Chemistry and Molecular Biophysics Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse—Intramural Research Program, National Institutes of Health, Baltimore, Maryland 21224, United States

2. Libin Cardiovascular Institute of Alberta, Cumming School of Medicine, University of Calgary, Calgary, Alberta T2N 4N1, Canada

3. Centre for Molecular Simulation, Department of Biological Sciences, University of Calgary, Calgary, Alberta T2N 1N4, Canada

4. Medicinal Chemistry Section, Molecular Targets and Medications Discovery Branch, National Institute on Drug Abuse—Intramural Research Program, National Institutes of Health, Baltimore, Maryland 21224, United States

5. Achlys Inc., 7-126 Li Ka Shing Center for Health and Innovation, Edmonton, Alberta T6G 2E1, Canada

Funder

Canadian Institutes of Health Research

National Institute on Drug Abuse

University of Calgary

IBM Centre for Advanced Studies

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.1c00856