Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers-Reference-Cited by-同舟云学术

Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers

Published:2023-10-16 Issue:43 Volume:8 Page:40864-40877
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Haddad Safa¹²,Oktay Lalehan¹²,Erol Ismail¹²,Şahin Kader³^ORCID,Durdagi Serdar¹²⁴^ORCID

Affiliation:

1. Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34353, Turkey

2. Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul 34353, Turkey

3. Department of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34734, Turkey

4. Molecular Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34353, Turkey

Funder

Istanbul Kalkinma Ajansi

Bahçesehir Üniversitesi

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.3c06074

Reference63 articles.

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4. Predicting hERG channel blockers with directed message passing neural networks

5. Clinical Evaluation of QT/QTc Prolongation and Proarrhythmic Potential for Nonantiarrhythmic Drugs: The International Conference on Harmonization of Technical Requirements for Registration of Pharmaceuticals for Human Use E14 Guideline