Degree-Based Topological Indices and Machine Learning for QSPR Modeling of Arthritis Drugs.

This study addresses the challenge of accurately predicting the physicochemical properties of arthritis drugs using simple graph-theoretic descriptors and evaluates whether combining degree-based topological indices with modern machine learning can outperform traditional linear QSPR models. Nine classical degree-based topological indices (including reformulated Zagreb indices, augmented Zagreb index, hyper-Zagreb index, forgotten index, symmetric division degree index, and inverse sum Zagreb index) were computed for 50 structurally diverse arthritis drugs ranging from NSAIDs (ibuprofen and diclofenac) and COX-2 inhibitors (celecoxib) to disease-modifying agents (methotrexate) and immunomodulators with experimental property values (boiling point, melting point, critical pressure, molar refractivity, topological polar surface area, and calculated LogP) obtained from ChemDraw and PubChem. Using these indices as input features, linear regression, Random Forest, and XGBoost models were developed and compared. XGBoost consistently outperformed the other methods across all properties, achieving for molar refractivity a mean absolute error (MAE) of 0.80787, a root-mean-square error (RMSE) of 1.13352, and coefficient of determination (