A novel approach is introduced for reliable prediction of the retention time of morphine and its derivatives, which have widespread use in medical and pharmaceutical applications. A core correlation is introduced based on the number of carbon and hydrogen atoms as well as the number of ester, noncyclic ether, and ketone functional groups from experimental data of 42 compounds. An improved correlation is developed to increase the reliability of the core correlation by inserting a correcting function. The reliability of two correlations is tested and compared with the best available complex method based on a backpropagation artificial neural network (BP-ANN) for further 15 compounds. The values of root mean squared error (RMSE) of core and improved correlations for the test set are 0.5681 and 0.4832 min, which are lower than those reported by BP-ANN (0.6052 min). Cross-validations of the improved correlation corresponding to the coefficients of determination for leave-one-out ( Q 2 LOO ) and the fivefold cross-validation ( Q 2 5CV ) are close to its the coefficient of determination ( R 2 = 0.982), which confirms goodness-of-fit, goodness-of-prediction, accuracy, and precision of the novel model.

中文翻译：

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不使用计算机编码的复杂描述符预测吗啡及其衍生物的保留时间

引入了一种新方法来可靠预测吗啡及其衍生物的保留时间，这些方法在医疗和制药应用中具有广泛的用途。基于碳和氢原子的数量以及酯、非环醚和酮官能团的数量，从 42 种化合物的实验数据中引入了核心相关性。通过插入校正函数，开发了改进的相关性以增加核心相关性的可靠性。测试了两个相关性的可靠性，并与基于反向传播人工神经网络 (BP-ANN) 的最佳可用复杂方法进行了比较，用于另外 15 种化合物。测试集的核心和改进相关性的均方根误差 (RMSE) 值为 0.5681 和 0.4832 分钟，低于 BP-ANN 报告的那些（0.6052 分钟）。与留一法决定系数 (Q 2 LOO ) 和五重交叉验证 ( Q 2 5CV ) 对应的改进相关性的交叉验证接近其决定系数 ( R 2 = 0.982)，这证实了新模型的拟合优度、预测优度、准确性和精度。