Computational Studies toward the Identification of CB2R-M1R Dual Modulators

The complex and multifactorial nature of different neurodegenerative disorders hampers the capacity to identify effective treatments. Therefore, instead of relying solely on monotherapies or combination therapies, which typically come with dosing complications and limited synergy, multitarget-directed ligand strategies have emerged as one of the most dynamic and promising approaches to improve outcomes for such diseases. This study sought to identify dual modulators that specifically target cannabinoid receptor type 2 (CB2R) and muscarinic acetylcholine receptor subtype 1 (M1R), two receptors involved in various physiological and neurological processes and frequently implicated in disorders like Alzheimer's, Parkinson's, and chronic pain. Herein, we utilized a comprehensive computational pipeline starting with a network pharmacology analysis to map the pharmacological landscape of the dual-targeted ligands. Thereafter, molecular descriptors were employed to uncover structural similarities between CB2R agonists and M1R-positive allosteric modulators. Promising candidates were further evaluated for their binding affinities to the corresponding receptors by molecular docking studies. Collectively, these integrated computational approaches yielded a shortlist of chemotypes with the potential for dual regulation of CB2R and M1R. These findings provide a computational foundation and potential chemical starting points for future experimental studies aimed at exploring CB2R-M1R dual modulation in intricate neurodegenerative disorders and related conditions.