Preliminary studies on apparent Mendelian psychotic disorders in consanguineous families

Kanwal, Ambreen; Sheikh, Sohail A.; Iftikhar, Amina; Naz, Sadaf; Pardo, José V.

doi:10.22541/au.162626101.13669766/v1

Cited by 1 publication

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Section: Data Collectionmentioning

confidence: 98%

“…Afterward, a categorization of inhibitors was produced by grouping them according to their chemical family. We found compounds belonging to flavonoids [27][28][29], alkaloids [30], triazole [31][32][33][34][35], thiadiazole [24,30,31,36], and coumarins [24,[37][38][39][40][41] chemical families. This dataset involved an IC 50 range from 0.009 micromolar (µM) to concentrations where a minimum inhibitory concentration for Hp urease enzyme was not determined, here termed non-urease inhibitors (nUIs).…”

Section: Data Collectionmentioning

confidence: 98%

“…Natural sources provide an immeasurable number of organic compounds with anti-urease activity, and these compounds could be used as a starting point to design stronger inhibitors. At present, there exist compounds reported with anti-urease activity such as polyphenols, flavonoids [27][28][29], alkaloids [30], triazole [31][32][33][34][35] thiadiazole [24,30,31,36], and coumarins [24,[37][38][39][40][41]. Despite the existence of a significant repertoire of urease inhibitors, there are compelling reasons to continue the pursuit of novel inhibitors.…”

Section: Introduction 1urease Enzyme and Its Implications In The Huma...mentioning

confidence: 99%

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Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

Morales,

Valdés-Muñoz,

González

et al. 2024

IJMS

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Urease, 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.

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Section: Data Collectionmentioning

confidence: 98%

Section: Data Collectionmentioning

confidence: 98%

Section: Introduction 1urease Enzyme and Its Implications In The Huma...mentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

Morales,

Valdés-Muñoz,

González

et al. 2024

IJMS

View full text Add to dashboard Cite

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Preliminary studies on apparent Mendelian psychotic disorders in consanguineous families

Cited by 1 publication

References 0 publications

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria

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