GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.

Journal: Pharmaceutical research
PMID: 40140128

Abstract

PURPOSE: The human Ether-a-go-go Related-Gene (hERG) encodes rectifying potassium channels that play a significant role during action potential repolarization of cardiomyocytes. Blockade of the hERG channel by off-target drugs can lead to long QT syndrome, significantly increasing the risk of proarrhythmic cardiotoxicity. Traditional hERG screening methods are effort-demanding and time-consuming. Thus, it is essential to develop computational methods to utilize the existing knowledge for faster and more accurate in silico screening. Although with wide use of deep learning/machine learning algorithms, existing computational models often rely on manually defined atomic features to represent atom nodes, which may overlook critical underlying information. Thus, we want to provide a new method to learn the atom representation automatically.

Authors

  • Yankang Jing
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.
  • Yiyang Zhang
    CEMS, NCMIS, HCMS, MDIS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
  • Guangyi Zhao
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Terence McGuire
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Jack Zhao
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Ben Gibbs
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Ganqian Hou
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, Pharmacometrics & Systems Pharmacology (PSP) Pharmacoanalytics, School of Pharmacy, University of Pittsburgh, 6411 Salk Hall, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Zhiwei Feng
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA.
  • Ying Xue
    Beijing Centers for Preventive Medical Research, Beijing 100013, China.
  • Xiang-Qun Xie

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