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

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, plays a key role in these responses. H1 blockers are essential components of cough syrups and flu medications. They are classified into two generations: first-generation H1 blockers, which are sedating and associated with numerous side effects, and second-generation blockers, which are non-sedating, generally less toxic, but may still exhibit cross-reactivity with other receptors.

METHODS: In this study, a comprehensive database of compounds was utilized, with fexofenadine serving 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.

RESULTS: 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, this study aims to facilitate the development of antihistamines with reduced adverse effects.