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Machine learning-driven classification of urease inhibitors leveraging physicochemical properties as effective filter criteria

dc.contributor.authorMorales, Natalia
dc.contributor.authorValdés-Muñoz, Elizabeth
dc.contributor.authorGonzález, Jaime
dc.contributor.authorValenzuela-Hormazábal, Paulina
dc.contributor.authorPalma, Jonathan M.
dc.contributor.authorGalarza, Christian
dc.contributor.authorCatagua-González, Ángel
dc.contributor.authorYáñez, Osvaldo
dc.contributor.authorPereira, Alfredo
dc.contributor.authorBustos, Daniel
dc.date.accessioned2024-05-28T20:32:12Z
dc.date.available2024-05-28T20:32:12Z
dc.date.issued2024
dc.identifier.urihttp://repositorio.ucm.cl/handle/ucm/5408
dc.description.abstractUrease, a pivotal enzyme in nitrogen metabolism, plays a crucial role in various microorganisms, including the pathogenic Helicobacter pylori. Inhibiting urease activity offers a promising approach to combating infections and associated ailments, such as chronic kidney diseases and gastric cancer. However, identifying potent urease inhibitors remains challenging due to resistance issues that hinder traditional approaches. Recently, machine learning (ML)-based models have demonstrated the ability to predict the bioactivity of molecules rapidly and effectively. In this study, we present ML models designed to predict urease inhibitors by leveraging essential physicochemical properties. The methodological approach involved constructing a dataset of urease inhibitors through an extensive literature search. Subsequently, these inhibitors were characterized based on physicochemical properties calculations. An exploratory data analysis was then conducted to identify and analyze critical features. Ultimately, 252 classification models were trained, utilizing a combination of seven ML algorithms, three attribute selection methods, and six different strategies for categorizing inhibitory activity. The investigation unveiled discernible trends distinguishing urease inhibitors from non-inhibitors. This differentiation enabled the identification of essential features that are crucial for precise classification. Through a comprehensive comparison of ML algorithms, tree-based methods like random forest, decision tree, and XGBoost exhibited superior performance. Additionally, incorporating the “chemical family type” attribute significantly enhanced model accuracy. Strategies involving a gray-zone categorization demonstrated marked improvements in predictive precision. This research underscores the transformative potential of ML in predicting urease inhibitors. The meticulous methodology outlined herein offers actionable insights for developing robust predictive models within biochemical systems.es_CL
dc.language.isoenes_CL
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Chile*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/cl/*
dc.sourceInternational Journal of Molecular Sciences, 25(8), 4303es_CL
dc.subjectUrease inhibitorses_CL
dc.subjectCheminformaticses_CL
dc.subjectMachine learninges_CL
dc.subjectPredictive modelinges_CL
dc.subjectBioactivity predictiones_CL
dc.subjectClassification modelses_CL
dc.titleMachine learning-driven classification of urease inhibitors leveraging physicochemical properties as effective filter criteriaes_CL
dc.typeArticlees_CL
dc.ucm.facultadFacultad de Ciencias de la Ingenieríaes_CL
dc.ucm.indexacionScopuses_CL
dc.ucm.indexacionIsies_CL
dc.ucm.indexacionScieloes_CL
dc.ucm.urimdpi.com/1422-0067/25/8/4303es_CL
dc.ucm.doidoi.org/10.3390/ijms25084303es_CL


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Atribución-NoComercial-SinDerivadas 3.0 Chile
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