Machine Learning-Driven QSRR Modeling of Albumin Binding in Fluoroquinolones: An SVR Approach Supported by HSA Chromatography

Human serum albumin (HSA) binding critically influences drug distribution and pharmacokinetics. In this study, HSA affinity chromatography was integrated with machine-learning-based quantitative structure–retention relationship (QSRR) modeling to elucidate structural determinants of albumin binding in a library of 115 fluoroquinolone (FQs) derivatives. Experimentally determined logkHSA values were obtained using biomimetic chromatography, and these were then used as modelling endpoints. Following descriptor reduction via Least Absolute Shrinkage and Selection Operator (LASSO) and systematic benchmarking of 42 regression algorithms, support vector regression (SVR) and nu-support vector regression (ν-SVR) with radial basis function kernels demonstrated superior predictive performance. A parsimonious 12-descriptor ν-SVR model achieved strong calibration and validation metrics (R2 = 0.916, Q2test = 0.823, concordance correlation coefficient (CCC) = 0.899) and satisfied Organisation for Economic Co-operation and Development (OECD) criteria, including applicability domain assessment. Shapley Additive exPlanations (SHAP)-based interpretation revealed that albumin binding is governed by a balance between hydrophobic surface area and distributed electronic properties, whereas excessive localized polarity and quaternary ammonium functionalities reduce affinity. This experimentally anchored and interpretable modeling framework provides mechanistic insight into HSA binding in fluoroquinolones and offers a robust tool for rational pharmacokinetic optimization. Furthermore, in order to make the model easily accessible to users, we have packaged it in the form of an online application.