Retention prediction in reversed-phase liquid chromatography using XGBoost-based quantitative structure-retention...

Reversed-phase liquid chromatography (RPLC) method development remains resource-intensive due to the iterative optimization required to achieve fit-for-purpose separations. While machine learning-enabled quantitative structure-retention relationships (QSRRs) offer predictive capabilities, current models suffer from poor stereoisomer discrimination, uncertain applicability domains, and limited practical integration. This work develops an XGBoost-based QSRR model using 43,329 retention measurements from 86 solutes on 13 stationary phases measured with isocratic mobile phases in the range of 2-90% organic content. The model combines standard 2D Mordred molecular descriptors with 37 custom stereochemical and geometric descriptors that distinguish R/S and E/Z configurations from SMILES strings. Feature selection reduced the descriptor set to 29, achieving a test-set root mean square error (RMSE) of 0.12 (ln selectivity scale), with the addition of custom isomer descriptors providing 2.5-fold improvement in stereoisomer prediction accuracy. The model demonstrated reliable interpolation (RMSE ≤ 0.05, selectivity scale) when molecular descriptor values are within the training-set bounds but showed degraded performance during extrapolation (RMSE 0.11-0.20, selectivity scale). Paired-solute analysis across the 13 stationary phases linked column-dependent prediction failures to descriptors omitted or pruned from the model, (e.g., acid-base character, halogen content, H-bond capacity). This computationally accessible framework provides systematic approaches for assessing prediction reliability across mobile phase compositions and stationary phase chemistries, establishing clear boundaries for trustworthy model application.