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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

Tran, Xuan-Truc Dinh (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
Phan, Tieu-Long (författare)
Univ Leipzig, Dept Comp Sci, Bioinformat Grp, Leipzig, Germany.;Univ Leipzig, Interdisciplinary Ctr Bioinformat, Leipzig, Germany.;Univ Southern Denmark, Dept Math & Comp Sci, Odense, Denmark.
To, Van-Thinh (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
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Tran, Ngoc-Vi Nguyen (författare)
Uppsala universitet,Institutionen för farmaci
Nguyen, Nhu-Ngoc Song (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
Nguyen, Dong-Nghi Hoang (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
Tran, Ngoc-Tam Nguyen (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
Truong, Tuyen Ngoc (författare)
Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam.
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Univ Med & Pharm Ho Chi Minh City, Fac Pharm, Ho Chi Minh City, Vietnam Univ Leipzig, Dept Comp Sci, Bioinformat Grp, Leipzig, Germany.;Univ Leipzig, Interdisciplinary Ctr Bioinformat, Leipzig, Germany.;Univ Southern Denmark, Dept Math & Comp Sci, Odense, Denmark. (creator_code:org_t)
Frontiers Media S.A. 2024
2024
Engelska.
Ingår i: Frontiers in Chemistry. - : Frontiers Media S.A.. - 2296-2646. ; 12
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
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  • 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

3D pharmacophore model
APJ receptor agonist
butina clustering algorithm
ensemble learning method
drug discovery

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