AI fragment based optimization of saffron and chamomile phytochemicals as aryl hydrocarbon receptor inhibitors for dementia therapy an integrated computational approach

Dementia represents a rapidly rising global health challenge as a progressive neurodegenerative disease with few options for disease-modifying treatments. The present study aimed to explore the leading phytochemicals from Crocus sativus (saffron) and Matricaria chamomilla (chamomile) and apply AI fragmentation on lead phytochemicals to target the aryl hydrocarbon receptor (AHR), an expertized target for dementia therapy. Bioactive compounds were screened from ISO 3632–2–2010 (E) specified for saffron and GC-MS specified for chamomile. Protein Network mapping, Density Functional Theory, Molecular docking, and molecular dynamics simulations were performed to determine the binding affinity and interactions stability of key phytochemicals with AHR, such as safranal and bisabolone oxide A. In-silico ADMET predictions of pharmacokinetics and toxicity showed good properties for these molecules. In addition, their structural and pharmacological properties were optimized to enhance drug-like features by using artificial intelligence (AI) generative model. Collectively, our findings highlight these AI-enhanced phytochemicals as promising AHR modulators with potentially therapeutic activities in pathological pathways that lead to neuroinflammation and oxidative stress involved in the pathogenesis of dementia. They offer an avenue for additional experimental validation and encourage further investigation of these leads as sources of new therapeutic modalities to treat neurodegenerative diseases.