Deep Learning Based Prediction of Gas Chromatographic Retention Indices for a Wide Variety of Polar and Mid-Polar Liquid Stationary Phases

Prediction of gas chromatographic retention indices based on compound structure is an important task for analytical chemistry. The predicted retention indices can be used as a reference in a mass spectrometry library search despite the fact that their accuracy is worse in comparison with the experimental reference ones. In the last few years, deep learning was applied for this task. The use of deep learning drastically improved the accuracy of retention index prediction for non-polar stationary phases. In this work, we demonstrate for the first time the use of deep learning for retention index prediction on polar (e.g., polyethylene glycol, DB-WAX) and mid-polar (e.g., DB-624, DB-210, DB-1701, OV-17) stationary phases. The achieved accuracy lies in the range of 16–50 in terms of the mean absolute error for several stationary phases and test data sets. We also demonstrate that our approach can be directly applied to the prediction of the second dimension retention times (GC × GC) if a large enough data set is available. The achieved accuracy is considerably better compared with the previous results obtained using linear quantitative structure-retention relationships and ACD ChromGenius software. The source code and pre-trained models are available online.

Gradient boosting for the prediction of gas chromatographic retention indices

The estimation of gas chromatographic retention indices based on compounds structures is an importantproblem. Predicted retention indices can be used in a mass spectral library search for the identificationof unknowns. Various machine learning methods are used for this task, but methods based on decisiontrees, in particular gradient boosting, are not used widely. The aim of this work is to examine the usability ofthis method for the retention index prediction. 177 molecular descriptors computed with Chemistry Development Kit are used as the input representation of a molecule. Random subsets of the whole NIST 17 database are used as training, test and validation sets. 8000 trees with 6 leaves each are used. A neural network with one hidden layer (90 hidden nodes) is used for the comparison. The same data sets and the set of descriptors are used for the neural network and gradient boosting. The model based on gradient boosting outperforms the neural network with one hidden layer for subsets of NIST 17 and for the set of essential oils.The performance of this model is comparable or better than performance of other modern retention prediction models. The average relative deviation is ~3.0%, the median relative deviation is ~1.7% for subsets of NIST 17. The median absolute deviation is ~34 retention index units. Only non-polar liquid stationary phases (such as polydimethylsiloxane, 5% phenyl 95% polydimethylsiloxane, squalane) are considered. Errors obtained with different machine learning algorithms and with the same representation of the molecule strongly correlate with each other.

6-Hydroxyisocaryophyllene and isocaryophyllenic acid from birches vegetative buds

The new isocaryophyllene derivatives were first detected in the birch vegetative buds. The structure of 6-hydroxyisocaryophyllene [(1R,4Z, 6R, 9S)-8-methylene-11,11-dimethylbicyclo[7.2.0]undec-4-ene-6-ol] isolated from the Betula pendula Roth. birch buds was determined by NMR spectroscopy. The structures of caryophyllenic acid and isocaryphyllenic acid isolated from the Betula grandifolia Litv., B. albo-sinensis Burk., B. fusca Pall.ex Georg, B. obscura A. Kotula, B. litwinowii Doluch., B. hallii Howell, B. grandifolia Litv. birch buds was determined by X-ray diffraction analysis. The physico-chemical characteristics and NMR data of 6-hydroxyisocaryophyllene, epoxide of 6-hydroxyisocaryophyllene and all the isolated acids are given. The gas chromatographic retention indices of all identified compounds were determined.