Quantitative structure property relationship study of postpartum depression medications using topological indices and regression models

Abstract

Postpartum depression (PPD) is a significant mental health condition experienced by the majority of women after childbirth. Understanding the underlying molecular properties of various medications used to treat PPD is essential in contributing to the efficacy of drug discovery and development. In this study, we aim to analyze the quantitative structural property relationships (QSPR) of PPD medications using topological indices and regression models. For a certain set of PPD medications, the topological indices are calculated. Through regression models, relationships between the calculated topological indices and pharmacological properties of the PPD medications are established. The results showcase significant correlations between certain topological indices and pharmacological properties. These discoveries offer insightful information about the molecular characteristics that support these medications' effectiveness. Drug discovery and development activities can be accelerated using the developed QSPR models to forecast the possible productiveness of novel PPD treatment candidates. Additionally, this research advances our knowledge of the molecular basis of PPD treatment and could help develop more focused and efficient therapeutic approaches. The legitimacy of the recently authorized medication Zurzuvae, which treats postpartum depression, is also verified here.