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image of Integrating Machine Learning and Pharmacophore Features for Enhanced Prediction of H1 Receptor Blockers

Abstract

Introduction

Histamine Type I Receptor Antagonists (H1 blockers) are widely used to mitigate histamine-induced inflammation, particularly in allergic reactions. Histamine, a biogenic amine found in endothelial cells, vascular smooth muscle, bronchial smooth muscle, and the hypothalamus, is a key player in these responses. H1 blockers are essential in cough syrups and flu medications and are divided into two generations: first-generation H1 blockers, which are sedating and have numerous side effects, and second-generation blockers, which are non-sedating and generally less toxic but may still exhibit cross-reactivity with other receptors.

Method

In this study, a comprehensive database of compounds was utilized alongside fexofenadine as a benchmark to discover compounds with potentially superior efficacy and reduced side effect profiles. In particular, multidimensional K-means clustering, a machine-learning technique, was applied to identify compounds with chemical structures similar to fexofenadine.

Result

Utilizing computational prediction of pharmacokinetic profile and molecular docking experiments, the action of these drugs on the H1 receptor was assessed. Furthermore, the cross-reactivity of antihistamines was investigated by conducting a structure-based pharmacophore feature analysis of the docked poses of highly toxic antihistamines with various receptors.

Conclusion

By identifying and proposing the removal of common toxic features, we aim to facilitate the development of antihistamines with fewer adverse effects.

© 2025 Bentham Science Publishers
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/content/journals/mc/10.2174/0115734064355393250121062539
2025-01-27
2025-06-30

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/content/journals/mc/10.2174/0115734064355393250121062539
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Integrating Machine Learning and Pharmacophore Features for Enhanced Prediction of H1 Receptor Blockers
Bentham Science Publishers ; https://doi.org/10.2174/0115734064355393250121062539
/content/journals/mc/10.2174/0115734064355393250121062539
/content/journals/mc/10.2174/0115734064355393250121062539
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  • Article Type:     Research Article
Keywords: machine learning ; H1 blockers ; multidimensional k-means clustering

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