Computational Exploration of Xanthones Derivatives as Potent Inhibitors of Monoamine Oxidase

Abstract

Monoamine oxidase A (MAOA) inhibitors (MAOIs-A) prevent the oxidative deamination of monoamine oxidase A and thus have therapeutic relevance in treating psychiatric and mental disorders such as depression. The search for effective therapeutic candidates for the MAOA protein has recently been strengthened with computer-assisted drug discovery (CADD) approaches based on the QSAR calculation method. In the present study, three QSAR chemometric methods were employed, including principal component analysis (PCA) for the selection of relevant descriptors, multiple linear regression (MLR), and multiple non-linear regression (MNLR) to formulate accurate and predictive quantitative structure-MAOA inhibitory effect relationship models for 34 xanthones. Reasonable values for R2, q2 and R2predwere obtained for the MLR model (R2 = 0.754, q2 = 0.527, and R2pred= 0.724) and the MNLR model (R2= 0.796 q2 = 0.623 and R2pred= 0.825). According to the OECD guidelines for validating QSAR models and the Gobraikh and Tropsha criteria, the linear and non-linear models developed meet internal and external requirements. Guided by the established model equations, a series of novel therapeutic candidates derived from xanthone with more potent MAO-A inhibitory activities were designed. Furthermore, the newly designed xanthones were subjected to molecular docking computational methodology. The findings of this research provide valuable information for discovering more efficient xanthone-based therapeutic agents for the MAO-A protein.