Empirical equations for the prediction of gas chromatographic retention indices for the DB-35MS stationary phase
Abstract
At the moment, most of the studies on the retention index prediction based on molecule structure are devoted to standard stationary phases: polydimethylsiloxane, 5%-phenyl-methylpolysiloxane, and polyethylene glycol. Retention index information for these stationary phases is contained in the NIST database, so a large training data set is available, and deep learning can be applied. This allows the creation of accurate and versatile retention index prediction models. However, other stationary phases are also actively used in research, for identification of components of complex mixtures using chromatography-mass spectrometry. The development of retention index prediction algorithms for these stationary phases could also be of great importance. In this paper, we consider the problem of predicting retention indices for the DB-35MS stationary phase (35%-phenyl-methylpolysiloxane). A data set of retention indices of 52 volatile organic compounds contained in lilac buds for this stationary phase is considered. Empirical equations are proposed that incorporate the retention index for the DB-5 stationary phase (5%-phenyl-methylpolysiloxane) predicted by deep learning and a number of molecular descriptors calculated using the RDKit framework. It was shown that the use of complex topological molecular descriptors, and features calculated using quantum chemistry does not provide a significant increase in accuracy compared to the simplest integer molecular descriptors, such as the number of bonds subject to internal rotation. At the same time, the use of the retention index for the DB-5 stationary phase predicted by deep learning as a molecular descriptor leads to a strong decrease in the prediction error compared to the use of only conventional molecular descriptors. When the retention indices predicted for the DB-624 stationary phase are used instead of the retention indices predicted for the DB-5 stationary phase, a relatively high prediction accuracy can also be achieved. Linear equations are presented that can be used in practice to calculate the retention indices of volatile plant compounds containing carbon, hydrogen, and oxygen for the DB-35MS stationary phase and similar stationary phases. A less accurate but more versatile equation is also presented that contains only the retention index predicted by deep learning for the DB-5 stationary phase as a molecular descriptor. The achieved values of the root-mean-square prediction error, the mean absolute prediction error, and the median absolute prediction error were 28.9, 19.3, and 11.8 units, respectively.
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