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

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作者： Safa,Haddad ^[1] ; Lalehan,Oktay ^[2] ; Ismail,Erol ^[1] ; Kader,Şahin ^[2] ; Serdar,Durdagi ^[1]

作者单位： Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul 34353, Turkey. ^[1] Computational Drug Design Center (HITMER), Bahçeşehir University, Istanbul 34353, Turkey. ^[2] Department of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34734, Turkey. ^[3] Molecular Therapy Lab, Department of Pharmaceutical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul 34353, Turkey. ^[4]

DOI 10.1021/acsomega.3c06074

PMID 37929100

发布时间 2023-11-07

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ACS omega

2023年8卷43期

40864-40877页

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

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

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