Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
American Chemical Society
2023-10-01
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| Series: | ACS Omega |
| Online Access: | https://doi.org/10.1021/acsomega.3c06074 |
| _version_ | 1797644417184563200 |
|---|---|
| author | Safa Haddad Lalehan Oktay Ismail Erol Kader Şahin Serdar Durdagi |
| author_facet | Safa Haddad Lalehan Oktay Ismail Erol Kader Şahin Serdar Durdagi |
| author_sort | Safa Haddad |
| collection | DOAJ |
| first_indexed | 2024-03-11T14:30:10Z |
| format | Article |
| id | doaj.art-66222584b4c94fb0b7066f220167d4a7 |
| institution | Directory Open Access Journal |
| issn | 2470-1343 |
| language | English |
| last_indexed | 2024-03-11T14:30:10Z |
| publishDate | 2023-10-01 |
| publisher | American Chemical Society |
| record_format | Article |
| series | ACS Omega |
| spelling | doaj.art-66222584b4c94fb0b7066f220167d4a72023-10-31T09:21:33ZengAmerican Chemical SocietyACS Omega2470-13432023-10-01843408644087710.1021/acsomega.3c06074Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based ClassifiersSafa Haddad0Lalehan Oktay1Ismail Erol2Kader Şahin3Serdar Durdagi4Computational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, TurkeyComputational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, TurkeyComputational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, TurkeyDepartment of Analytical Chemistry, School of Pharmacy, Bahçeşehir University, Istanbul, TurkeyComputational Biology and Molecular Simulations Laboratory, Department of Biophysics, School of Medicine, Bahçeşehir University, Istanbul, Turkeyhttps://doi.org/10.1021/acsomega.3c06074 |
| spellingShingle | Safa Haddad Lalehan Oktay Ismail Erol Kader Şahin Serdar Durdagi Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers ACS Omega |
| title | Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers |
| title_full | Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers |
| title_fullStr | Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers |
| title_full_unstemmed | Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers |
| title_short | Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-Based Classifiers |
| title_sort | utilizing heteroatom types and numbers from extensive ligand libraries to develop novel herg blocker qsar models using machine learning based classifiers |
| url | https://doi.org/10.1021/acsomega.3c06074 |
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