A new molecular structure representation, molecular hologram, is employed to investigate the quantitative relationship between gas chromatographic retention indices and molecular structures for 41 methylesterified org...A new molecular structure representation, molecular hologram, is employed to investigate the quantitative relationship between gas chromatographic retention indices and molecular structures for 41 methylesterified organophosphorus compounds (OPs). The quantitative structure-retention relationship (QSRR) model has been constructed for GC-RI of the selected OPs through partial least squares regression, which shows high statistical quality and predictive value with non-cross validation correlation coefficient r2 of 0.994, and cross validation correlation coefficient q2LOO values of 0.984. In order to verify the robustness and prediction capacity of the model, 30 OPs were randomly selected from the database as the training set, while the rest were used as the testing set. The result of PLS regressive analysis of the training set yields r2 of 0.995 and q2LOO of 0.982, suggesting the excellent ability to predict the GC-RIs of OPs in the testing set. Furthermore, the retention behavior of the compounds in GC stationary phase is discussed, and the effects of different groups on the OP side-chain in the interaction between OPs and the stationary phase are explored using HQSAR color code, which provides useful guideline for the retention rules of OPs and related compounds.展开更多
基金Supported by the National Natural Science Foundation Key Project of China (Grant No. 20737001)the National Natural Science Foundation of China (Grant Nos. 20477018 and 20477017)
文摘A new molecular structure representation, molecular hologram, is employed to investigate the quantitative relationship between gas chromatographic retention indices and molecular structures for 41 methylesterified organophosphorus compounds (OPs). The quantitative structure-retention relationship (QSRR) model has been constructed for GC-RI of the selected OPs through partial least squares regression, which shows high statistical quality and predictive value with non-cross validation correlation coefficient r2 of 0.994, and cross validation correlation coefficient q2LOO values of 0.984. In order to verify the robustness and prediction capacity of the model, 30 OPs were randomly selected from the database as the training set, while the rest were used as the testing set. The result of PLS regressive analysis of the training set yields r2 of 0.995 and q2LOO of 0.982, suggesting the excellent ability to predict the GC-RIs of OPs in the testing set. Furthermore, the retention behavior of the compounds in GC stationary phase is discussed, and the effects of different groups on the OP side-chain in the interaction between OPs and the stationary phase are explored using HQSAR color code, which provides useful guideline for the retention rules of OPs and related compounds.
