Computational Approaches to Drug Discovery in Depression
Kalpesh Ramdas Patil, Aman Babanrao Upaganlawar, Akhil A. Nagar, Kuldeep U. Bansod · 2025
Depression is stated as the most prevalent form of mental disorder around the globe by the World Health Organization (WHO). The etiopathogenesis of depression is complex and the disease manifestations vary with the involvement of multiple receptors. Several hypotheses and signaling pathways are described for depression. Computational attitudes are becoming a key component in the process of identification and development of new druggable molecules. Computational attitudes are cost-effective solutions for the discovery of novel leads targeting a variety of human ailments including depression. These computational approaches assist the drug discovery process from identifying leads to discovering clinical biomarkers. Computational attitudes like machine learning, artificial intelligence, docking simulations, and network pharmacology are vital resources for obtaining realistic insights and promoting decision-making in the drug discovery process. Applications of computational approaches range from compound screening, and de novo drug design, to the forecast of drug response and drug interactions. The present chapter focuses on various computational approaches for drug discovery in depressive disorders.