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 crossreactivity 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.