ArticleFrontiers in chemistry2026
SMILES-based degree molecular descriptors and machine learning for QSPR modeling of anti-alkaptonuria drugs.
Article in Frontiers in chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Quantitative Structure-Property Relationship (QSPR) modelling provides an efficient computational framework for predicting physicochemical properties of drug molecules when experimental data are limited. In this study, we investigate the predictive capability of degree-based topological indices (TIs) derived from SMILES (Simplified Molecular Input Line Entry System) representations for modelling physicochemical properties of anti-alkaptonuria drugs. Nine representative compounds, including Nitisinone, Ascorbic Acid, Ibuprofen, Naproxen, Paracetamol, Tramadol, Methotrexate, Sulfasalazine, and Glucosamine, were analysed using several molecular descriptors such as molecular weight, logP, hydrogen bond donors and acceptors, rotatable bonds, and polar surface area. A total of 58 regression models were developed using Linear Regression (LR) and two machine learning algorithms, Random Forest (RF) and Extreme Gradient Boosting (XGBoost, abbreviated XGB). Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and the coefficient of determination
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