Leveraging Network Pharmacology, Molecular Docking to Discover Novel Multitarget Antidepressants

Depression, or major depressive disorder (MDD), is linked to suicidal tendencies and is becoming increasingly common, making it a critical mental health issue that requires attention. The rise in its prevalence is attributed to significant lifestyle and environmental shifts. Although traditional antidepressants that target monoaminergic systems are available, they face challenges such as delayed effects, side effects, and treatment-resistant depression (TRD), due to their limited mechanisms of action, inconsistent response rates, and the complex nature of depression. Recent studies indicate that MDD involves intricate neurobiological processes, including dysregulation of the hypothalamic pituitary adrenal axis (HPA), neuroinflammation, oxidative stress, and neurotransmitter imbalances. In recent years, advancements in bioinformatics, combined with network pharmacology (NP), molecular docking, and computer-aided drug design (CADD), have offered a comprehensive platform for discovering and validating new antidepressant candidates with multitarget effects. These in silico tools enable the elucidation of compound target pathway networks, facilitate virtual screening, and accelerate the optimization of lead molecules, particularly from natural sources such as medicinal plants. Phytoconstituents, such as flavonoids and alkaloids, exhibit promising antidepressant potential through modulation of key pathways including PI3K/Akt, MAPK, cAMP/CREB, and HIF-1α-VEGF. This review aims to provide the information obtained from the above mentioned in silico tools to identify appropriate targets and novel antidepressants, aiming to improve the safety and efficacy of existing drugs and develop new therapies from ethnobotanicals by exploring multitarget approaches.