Construction of QSRR model for reversed phase chromatography of steroid compounds based on quantum chemical calculation

Steroid compounds widely exist in traditional Chinese medicines, food, and are often illegally added to cosmetics. Their residues and abuse pose serious threats to public health and raise critical issues in drug safety and quality control. Therefore, developing efficient and accurate qualitative identification methods is essential. Although UHPLC-HRMS is currently the most common identification method, it has limitations such as time-consuming optimization of chromatographic conditions, high costs of reference standards, incomplete databases, and difficulties in distinguishing isomers. Thus, there is an urgent need for effective retention behavior prediction methods to assist qualitative analysis. Quantitative structure-retention relationship (QSRR) models enable theoretical prediction of chromatographic retention times. However, most traditional models rely directly on density functional theory (DFT) for conformational optimization, resulting in low computational efficiency. This study proposes a two-step approach: first, rapidly screen stable preferred conformations using the (parametric method 6) PM6 semi-empirical method; then, calculate molecular descriptors via DFT. Using only 11 model compounds and two molecular descriptors, we developed a QSRR model that effectively predicts the retention times of steroid compounds. The model was externally validated using five steroid compounds including a pair of isomers, yielding a predictive determination coefficient Q²_ext of 0.9618, with all relative errors below 4% and a mean absolute relative error (MARE) of 1.22%, demonstrating good predictive ability for unknown steroid compounds. This method improves computational efficiency, overcomes traditional modeling limitations, supports steroid isomer discrimination and qualitative identification, and provides insights into the reversed-phase separation mechanisms of structural analogs and separation optimization.