In-silico exploring pathway and mechanism-based therapeutics for allergic rhinitis: Network pharmacology, molecular docking, ADMET, quantum chemistry and machine learning based QSAR approaches.

Allergic rhinitis is a devastating health complication that interrupts the quality of daily life and significantly affects around 40 % of the population worldwide. Despite the availability of various treatment options, many patients continue to struggle with persistent symptoms and side effects, highlighting the need for innovative therapeutic approaches. Therefore, identifying pathway and mechanism-based targeted therapies with more effective and fewer side effects could aid current therapeutics and provide novel therapeutic advantages. This study aimed to identify potential drug candidates for allergic rhinitis treatment by employing in-silico approaches, including network pharmacology, molecular docking, ADMET, similarity, pharmacophore modeling, quantum chemistry, and machine learning-based QSAR modeling. From three traditionally used medicinal plants known as allergic rhinitis curing, Xanthium strumarium, Magnolia liliiflora, and Tylophora indica, 241 compounds were obtained, and their favorable ADMET properties were analyzed. Network pharmacology revealed 203 potential therapeutic targets, with 15 hub targets identified through protein-protein interaction network analysis and most of them play key roles in inflammatory and immune pathways confirmed by KEGG pathway analysis. Molecular docking, similarity testing, and pharmacophore modeling studies identified promising compounds Quercetin, Yinyanghuo E, Uralenin, CID:90643991, CID:42607537, CID:76329670, Heracetin, and Fisetin exhibiting strong binding affinities with key regulatory targets, NFKB1, TRAF6, and key cytokines IL5, and IL6 that directly and indirectly involved in allergic reactions. Quantum chemistry calculations revealed favorable electronic properties and reactivities of these compounds. The machine learning-based QSAR model predicted IC50 < 50 nM for almost all compounds, indicating highly potent inhibitors. Hence, this in-silico study identified some novel promising drug candidates for treating allergic rhinitis by targeting crucial inflammatory and immune pathways, offering improved treatment outcomes and reduced side effects, subject to further experimental validation.