Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study-Reference-Cited by-同舟云学术

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Machine learning and deep learning approaches for enhanced prediction of hERG blockade: a comprehensive QSAR modeling study

Published:2024-07-02 Issue:7 Volume:20 Page:665-684
ISSN:1742-5255
Container-title:Expert Opinion on Drug Metabolism & Toxicology
language:en
Short-container-title:Expert Opinion on Drug Metabolism & Toxicology

Author:

Liu Jie¹,Khan Md Kamrul Hasan¹,Guo Wenjing¹,Dong Fan¹,Ge Weigong¹,Zhang Chaoyang²,Gong Ping³,Patterson Tucker A.¹,Hong Huixiao¹

Affiliation:

1. National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA

2. School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, USA

3. Environmental Laboratory, US Army Engineer Research and Development Center, Vicksburg, MS, USA

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/17425255.2024.2377593

Reference85 articles.

1. Drug Withdrawal Due to Safety: A Review of the Data Supporting Withdrawal Decision

2. İlaca Bağlı Kardiyovasküler Risk: Geri Çekilen İlaçların Retrospektif Değerlendirilmesi

3. hERG channel function: beyond long QT

4. A history of the role of the hERG channel in cardiac risk assessment

5. Novel 384-Well Population Patch Clamp Electrophysiology Assays for Ca2+-Activated K+ Channels

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