In Silico Identification of Novel GABRA2 Inhibitors Leveraging the Power of Virtual Screening

Anxiety, one of the most prevalent neuropsychiatric disorders, often remains underdiagnosed due to its subtle and varied symptoms. Among the key players in anxiety regulation, the GABRA2 receptor subunit of the GABA receptor system stands out for its critical role in maintaining neuronal inhibition and promoting calmness. Despite its significance, the GABRA2 receptor has been largely unexplored in drug discovery. Current therapeutics, including Benzodiazepines like Diazepam, target GABA receptors but are associated with undesirable side effects such as sedation and cognitive dysfunction, underscoring the need for safer alternatives. This study presents a pioneering effort to explore the GABRA2 receptor using advanced computational techniques, including virtual screening and molecular docking. By leveraging a novel and comprehensive in silico approach, we identified promising drug candidates as potential alternatives to current benzodiazepine-based therapies. Our findings not only emphasize the untapped therapeutic potential of targeting GABRA2 in anxiety management but also validate the power of computational methodologies in accelerating drug discovery.