Machine learning approaches to study the structure-activity relationships of LpxC inhibitors
Abstract: Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation .... https://www.excli.de/excli/article/view/6356
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Machine learning approaches to study the structure-activity relationships of LpxC inhibitors ; volume:22 ; year:2023
EXCLI journal ; 22 (2023)
- Urheber
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Yu, Tianshi
Chong, Li Chuin
Nantasenamat, Chanin
Anuwongcharoen, Nuttapat
Piacham, Theeraphon
- DOI
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10.17179/excli2023-6356
- URN
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urn:nbn:de:101:1-2502171611548.003642845073
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:34 MESZ
Datenpartner
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Beteiligte
- Yu, Tianshi
- Chong, Li Chuin
- Nantasenamat, Chanin
- Anuwongcharoen, Nuttapat
- Piacham, Theeraphon