AI‐Driven Optimization of Cetirizine‐Derived Ligands Targeting Histamine H1 and H2 Receptors: A Computational Drug Design Study

ABSTRACT Allergic diseases, such as rhinitis, asthma, and urticaria, are prevalent conditions that are highly burdensome. These allergic responses are mediated by the action of histamine on the HRH1 and HRH2 histamine receptors. Cetirizine is a second‐generation antihistamine used extensively for managing allergic manifestations and works predominantly as an HRH1 receptor blocker. Its selective binding to the HRH1 receptor restricts its use for treating conditions involving HRH1 and HRH2 receptors. This current study highlights the ability of AI‐driven drug design to increase cetirizine binding affinity for both HRH1 and HRH2 receptors. Through computational methods like receptor retrieval, validation, molecular docking, and ADMET analysis, ligands that had better binding profiles over cetirizine is created by AI. The AI‐generated ligand 2 had better binding affinities to both targets than the commercial control cetirizine (−9.0 and −7.2 kcal/mol for HRH1 and HRH2 versus −7.6 and −5.9 kcal/mol). Pharmacokinetic profile analysis using ADMET showed that the AI‐generated ligands exhibited a good pharmacokinetic profile having good Lipophilicity, GI absorption, no violation of Lipinski's rule and less toxicity than cetirizine. Molecular dynamics simulations reveal the stable binding and favorable interactions of an AI‐generated lead with HRH1 and HRH2, highlighting its potential for further drug development. The results predicted that AI‐driven ligands may provide a good and better safer option than cetirizine and be helpful in other allergic conditions.