Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists
Introduction: 3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design.Methods: Our research summarized the key...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-04-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2024.1382319/full |
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| author | Xuan-Truc Dinh Tran Tieu-Long Phan Tieu-Long Phan Van-Thinh To Ngoc-Vi Nguyen Tran Nhu-Ngoc Song Nguyen Dong-Nghi Hoang Nguyen Ngoc-Tam Nguyen Tran Tuyen Ngoc Truong |
| author_facet | Xuan-Truc Dinh Tran Tieu-Long Phan Tieu-Long Phan Van-Thinh To Ngoc-Vi Nguyen Tran Nhu-Ngoc Song Nguyen Dong-Nghi Hoang Nguyen Ngoc-Tam Nguyen Tran Tuyen Ngoc Truong |
| author_sort | Xuan-Truc Dinh Tran |
| collection | DOAJ |
| description | Introduction: 3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design.Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening.Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031.Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures. |
| first_indexed | 2024-04-24T08:58:58Z |
| format | Article |
| id | doaj.art-be521b7d772d4697b1cc27b5eae342df |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-04-24T08:58:58Z |
| publishDate | 2024-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-be521b7d772d4697b1cc27b5eae342df2024-04-16T04:46:52ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462024-04-011210.3389/fchem.2024.13823191382319Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonistsXuan-Truc Dinh Tran0Tieu-Long Phan1Tieu-Long Phan2Van-Thinh To3Ngoc-Vi Nguyen Tran4Nhu-Ngoc Song Nguyen5Dong-Nghi Hoang Nguyen6Ngoc-Tam Nguyen Tran7Tuyen Ngoc Truong8Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamBioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Leipzig, GermanyDepartment of Mathematics and Computer Science, University of Southern Denmark, Odense, DenmarkFaculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Pharmacy, Uppsala University, Uppsala, SwedenFaculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamFaculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, Ho Chi Minh City, VietnamIntroduction: 3D pharmacophore models describe the ligand’s chemical interactions in their bioactive conformation. They offer a simple but sophisticated approach to decipher the chemically encoded ligand information, making them a valuable tool in drug design.Methods: Our research summarized the key studies for applying 3D pharmacophore models in virtual screening for 6,944 compounds of APJ receptor agonists. Recent advances in clustering algorithms and ensemble methods have enabled classical pharmacophore modeling to evolve into more flexible and knowledge-driven techniques. Butina clustering categorizes molecules based on their structural similarity (indicated by the Tanimoto coefficient) to create a structurally diverse training dataset. The learning method combines various individual pharmacophore models into a set of pharmacophore models for pharmacophore space optimization in virtual screening.Results: This approach was evaluated on Apelin datasets and afforded good screening performance, as proven by Receiver Operating Characteristic (AUC score of 0.994 ± 0.007), enrichment factor of (EF1% of 50.07 ± 0.211), Güner-Henry score of 0.956 ± 0.015, and F-measure of 0.911 ± 0.031.Discussion: Although one of the high-scoring models achieved statistically superior results in each dataset (AUC of 0.82; an EF1% of 19.466; GH of 0.131 and F1-score of 0.071), the ensemble learning method including voting and stacking method balanced the shortcomings of each model and passed with close performance measures.https://www.frontiersin.org/articles/10.3389/fchem.2024.1382319/full3D pharmacophore modelAPJ receptor agonistbutina clustering algorithmensemble learning methoddrug discovery |
| spellingShingle | Xuan-Truc Dinh Tran Tieu-Long Phan Tieu-Long Phan Van-Thinh To Ngoc-Vi Nguyen Tran Nhu-Ngoc Song Nguyen Dong-Nghi Hoang Nguyen Ngoc-Tam Nguyen Tran Tuyen Ngoc Truong Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists Frontiers in Chemistry 3D pharmacophore model APJ receptor agonist butina clustering algorithm ensemble learning method drug discovery |
| title | Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists |
| title_full | Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists |
| title_fullStr | Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists |
| title_full_unstemmed | Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists |
| title_short | Integration of the Butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand-based model: an in silico investigation of apelin agonists |
| title_sort | integration of the butina algorithm and ensemble learning strategies for the advancement of a pharmacophore ligand based model an in silico investigation of apelin agonists |
| topic | 3D pharmacophore model APJ receptor agonist butina clustering algorithm ensemble learning method drug discovery |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2024.1382319/full |
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