Revisiting the LSER Approach in the Era of Machine Learning: Insights from IAM Chromatography

Abstract

The present study demonstrates the integration of the linear solvation energy relationship (LSER) concept with machine learning (ML) methodologies to improve the predictive and interpretative capabilities of chromatographic retention modeling. Immobilized artificial membrane (IAM) chromatography was employed as a model biochromatographic system, and an in-house library of 993 structurally diverse compounds, with experimentally determined chromatographic hydrophobicity index of IAM (CHIIAM), was used to train LSER-ML models. LSER descriptors were calculated using Absolv and extended with ionization-state descriptors to evaluate the applicability of the LSER framework for realistic in silico virtual screening scenarios. Several regression algorithms were tested, including linear, neighborhood-based, kernel-based, and ensemble tree-based models. Among them, the support vector regression with the radial basis function kernel (SVR-RBF) demonstrated the most balanced performance across all validation metrics of R2train = 0.884, R2test = 0.853, and Q2cv = 0.811, achieving predictive errors (RMSEtrain = 5.546, RMSEtest = 4.609, and RMSEcv = 6.989) close to the analytical uncertainty. Model interpretability was achieved using SHapley Additive exPlanations (SHAP), which confirmed the mechanistic relevance of the Abraham descriptors and the dominant contribution of hydrophobic volume and hydrogen-bonding properties to IAM retention. Applicability domain was verified with a Williams plot (±3 standardized residuals and leverage threshold h*). The results indicate that the proposed LSER-ML approach provides an interpretable, robust, and generalizable tool for modeling membrane-mimetic chromatographic systems and can be effectively applied in virtual screening and property-based molecular design.