Leveraging topological indices and machine learning for advanced prediction of antidepressant drug properties

This study investigates the efficacy of eight multiplicative degree-based and three classical degree-based topological indices in Quantitative Structure-Property Relationship (QSPR) models for predicting critical physicochemical properties of 38 antidepressant drugs. Molecular structures of compounds including Bupropion, Amitriptyline, and Fluoxetine were translated into numerical descriptors using indices such as Multiplicative Sum Zagreb, Multiplicative Sombor, and the First Zagreb index. These descriptors were integrated with machine learning algorithms: Random Forest, XGBoost, and linear regression to forecast boiling points, melting points, critical temperature, critical volume, and molar refractivity. Results revealed that the XGBoost algorithm significantly outperformed other methods, achieving superior predictive accuracy with the lowest error metrics (e.g., for boiling point: MAE = 8.60, RMSE = 12.40, [Formula: see text]). Among the topological indices, the First Zagreb index ([Formula: see text]) emerged as the most robust descriptor, demonstrating the strongest correlations with key properties (e.g., [Formula: see text] with critical volume, [Formula: see text] with molar refractivity). Linear regression models further confirmed the significance of [Formula: see text] and other indices, with high statistical significance ([Formula: see text]) in most cases. This interdisciplinary approach demonstrates the potent synergy of graph-theoretic indices and advanced machine learning in pharmaceutical research, offering a powerful strategy to accelerate drug discovery and optimize the design of novel therapeutic agents.