Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations-Reference-Cited by-同舟云学术

Assessing hERG1 Blockade from Bayesian Machine-Learning-Optimized Site Identification by Ligand Competitive Saturation Simulations

Published:2020-11-16 Issue:12 Volume:60 Page:6489-6501
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Mousaei Mahdi¹,Kudaibergenova Meruyert¹,MacKerell Alexander D.²^ORCID,Noskov Sergei¹^ORCID

Affiliation:

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

2. Computer-Aided Drug Design Center, Department of Pharmaceutical Science, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States

Funder

National Heart, Lung, and Blood Institute

University of Calgary

Institute of Circulatory and Respiratory Health

National Institute of General Medical Sciences

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.0c01065

Reference92 articles.

1. hERG K+Channels: Structure, Function, and Clinical Significance

2. hERG Channels: From Antitargets to Novel Targets for Cancer Therapy

3. ERG K+ channels modulate the electrical and contractile activities of gallbladder smooth muscle

4. Expression and function of KCNH2 (HERG) in the human jejunum

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