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Prediction of Retention Time of Morphine and Its Derivatives Without Using Computer-Encoded Complex Descriptors

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Abstract

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 (Q2LOO) and the fivefold cross-validation (Q25CV) are close to its the coefficient of determination (R2 = 0.982), which confirms goodness-of-fit, goodness-of-prediction, accuracy, and precision of the novel model.

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Acknowledgement

We would like to thank the Malek-ashtar University of Technology for supporting this work. We want also to thank Professor Dr. Paola Gramatica for providing QSARINS version 2.2.4.

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This study was self-funded, no fund is received.

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  1. Faculty of Applied Sciences, Malek Ashtar University of Technology, Shahin-shahr, Iran

    Mohammad Hossein Keshavarz, Zeinab Shirazi & Mohammad Ali Rezayat

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  1. Mohammad Hossein Keshavarz
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  2. Zeinab Shirazi
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  3. Mohammad Ali Rezayat
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Correspondence to Mohammad Hossein Keshavarz.

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Keshavarz, M.H., Shirazi, Z. & Rezayat, M.A. Prediction of Retention Time of Morphine and Its Derivatives Without Using Computer-Encoded Complex Descriptors. Chromatographia 84, 87–96 (2021). https://doi.org/10.1007/s10337-020-03975-z

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