Integration of graph-based descriptors with machine learning algorithm for QSPR modeling of fluoroquinolones

In this article, QSPR analysis of antibiotic drugs such as Captopril, Norfloxacin, Dorzolamide, Saquinavir, Indinavir, Ritonavir, Oseltamivir, Zanamivir, Imatinib, Zolmitriptan, and Aliskiren has been investigated. The following properties were considered: density (D), refractive index (IR), molar refractivity (MR), polarizability (POL) surface tension (ST), Bioconcentration Factor (BCF) and molar volume (MV). The modified reverse counterparts were applied to model the relationship between molecular structure and physicochemical properties as effective descriptors for the prediction of drug behavior. Predictive models were thereafter developed, focusing on the role these indices might play in capturing structural influences, aided by different types of statistical regression models, including both linear and cubic, joined lately by the Extreme Gradient Boosting (XGBoost) machine learning algorithm, a novel tree-based ensemble model useful for testing in comparison with traditional approaches that rely on regression. On such grounds, it clearly follows from this that robustness in the performance of adjusted topological indices is achieved when approaches are combined with some state-of-the-art regression methods. Among these, XGBoost had the best predictive capability, nonlinearly modeling the relationships between structure and property more effectively than any other regression model. This study focuses on the potential for integrating degree-based topological indices with the machine learning tolerance of QSPR modeling.