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Abstract A study is performed of the effect the way of standardizing the molecular structure and parameters of calculating molecular fingerprints has on the accuracy of predicting constants of acidity. It is shown that standardization (i.e., the choice of the tautomeric form and the way of writing the structure of the molecule) using OpenEye QuacPac gives the best results, but the RDKit library allows comparable accuracy to be achieved. It is established that how the charge state is chosen has a great effect on the accuracy of predictions. The accuracy of predictions depending on the radius (size of substructures) of circular molecular fingerprints is studied, and the best results are achieved using radius r = 2. A random forest, a machine learning algorithm, is used. It is also shown that the use of support vectors ensures fairly high accuracy when optimizing hyperparameters.

It is known that highly charged droplets formed in the course of electrospraying disintegrate into a number of smaller droplets. The criteria for the instability and disintegration of conducting liquid droplets in the course of electrospraying have been considered in this article. Some forms of the perturbation of a spherical liquid droplet have been shown for the case when the Rayleigh stability criterion is exceeded. The analysis of the development of charge instability has shown that a quasi-stable state may exist during the disintegration of a liquid droplet in the region of instability according to the Rayleigh criterion.

The liquid fraction of tire pyrolysis products was comprehensively analyzed by gas chromatography–mass spectrometry and two-dimensional gas chromatography. The direct (nonpolar–polar) и and inverse (polar–nonpolar) column combinations were used. The inverse combination allows efficient separation of alkanes and alkenes. Aliphatic hydrocarbons constitute no more than 24 wt % of the liquid fraction, with the remainder being aromatic, mainly monoaromatic, compounds. The sulfur content of the liquid fraction is about 0.7 wt %; sulfur is mainly present in the form of thiophene derivatives.

Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.

A technique of molecular statistical calculations, developed and improved with the active participation of workers at the Institute of Physical Chemistry and Electrochemistry, allows the accurate estimation of the chromatographic retention of analytes. The resulting version of molecular statistical method (with regard to conformational non-rigidity) is used to predict the values of alkylbenzene retention under conditions of gas adsorption chromatography. The accuracy of estimating the retention indices is compared to results from current machine learning approaches of predicting retention (for gas-liquid chromatography). The effectiveness of separating structural hydrocarbon isomers on different columns is also compared.

Gas chromatography is a widely used method in analytical chemistry and metabolomics. Using gas chromatography, vaporizable compounds can be separated for their further identification. Retention indices are standardized values that depend only on a chemical structure of a compound and on a stationary phase and characterize the retention of a compound in a chromatographic system. Retention index prediction is an important task because databases contain experimental values for a small fraction of all possible molecules, while this information is usable for untargeted analysis. In this work, we consider four machine learning models for retention index prediction: 1D and 2D convolutional neural networks, deep residual multilayer perceptron, and gradient boosting. String representation of the molecule, 2D representation of the chemical structure, molecular descriptors and fingerprints, and molecular descriptors are used as inputs of these four models, respectively, along with information about the stationary phase. The first and third models show the best performance, while the other two perform slightly worse. The models predict retention index values for various standard and semi-standard non-polar stationary phases. Further improvement in performance was achieved using a linear model that uses the results of four previous models as inputs (model stacking). The models were tested using various diverse data sets: flavor compounds, essential oils, metabolomics-related compounds. Achieved accuracy: median absolute and percentage errors – 6–40 units and 0.8-2.2%. Accuracy depends on a test data set. The stacking model outperforms previously reported approaches for all test data sets. Parameters of a pre-trained model and some source code are provided.

A comparative study of adsorption of isomeric alkylbenzenes, alkylphenols, and alkanes on carbon sorbents from a gas and a liquid phase using an extended variant of molecular statistics calculations is carried out. In simulation, conformational non-rigidity of molecules is taken into account. This method is shown to satisfactorily estimate proportions between the adsorption characteristics for isomers of hydrocarbons adsorbed on graphitized thermal carbon black from a gas phase. In the case of adsorption from a liquid phase on porous graphitized carbon, additionally, the effect of a solvent and polarizability of the sorbent surface by charged sites of a sorbate molecule are considered. It is demonstrated that, for correct prediction of the ratio between the Henry constants for adsorption of isomers of alkylphenols and alkylbenzenes from solutions, it is necessary to consider both these factors.

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.

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.

Investigation of the physical chemistry of adsorption by carbon sorbents is a very important task from both theoretical and practical points of view. The thermodynamics of adsorption is affected by defects on the sorbent surface, including oxygen-containing functional groups. In this work, adsorption of a series of aromatic and aliphatic amines and nitriles by the surface of graphitized thermal carbon black is simulated using the Monte Carlo method. The results of calculations are compared to the experimental data. The cases of isolated and paired arrangement of functional groups are considered, and regularities of interaction between different classes of nitrogen-containing compounds and such defects are revealed. On the basis of comparison with the experimental data, assumptions about the presence and distribution of hydroxyls on the surface of graphitized thermal carbon black are made.

The influence of the surface chemistry of nonpolar sorbents (silica gel C18 and porous graphitized carbon), temperature, and liquid phase composition on the parameters of chromatographic retention of S,N-derivatives of 1,1-dimethylhydrazine (thiosemicarbazides) has been studied under the conditions of liquid chromatography. Experimental data have been compared with the results of preliminary estimates obtained for the adsorption characteristics of thiosemicarbazides using a molecular-statistical approache and determination of lipophility factor. Satisfactory agreement has been revealed between the experimental data and theoretical predictions. It has been shown that mixtures of ethyl-, allyl-, and phenylthiosemicarbazide are most efficiently separated by chromatography with the use of porous graphitized carbon packed into a Hypercarb column. The selectivity of this sorbent is 1.5 times higher than that of octadecyl silica gel packed into a Zorbax Eclipse XDB C18 column.

The adsorption of polychlorinated aromatic hydrocarbons on graphitized thermal carbon black was simulated by a molecular-statistical method. The results of calculations were compared with data on chromatographic retention under gas chromatography conditions. A version that took into account the conformational flexibility of molecules was used for polychlorinated biphenyls. Different computational chemistry methods (classical molecular dynamics and semiempirical quantum methods) for evaluating the internal energy of a molecule were considered. It was found that the use of a molecular-statistical method and the AM1 semiempirical method for estimating the internal energy of molecules makes it possible to correctly predict the order of elution of isomeric dichlorobiphenyls. It was demonstrated that this approach can be used to confirm the assignment of peaks in a chromatogram to particular isomers.

S,N-derivatives of alkylhydrazines, including 1,1-dimethylhydrazine, have been synthesized for the quantitative analysis of trace impurities of hydrazines in environmental objects. The molecular-statistical method has been used to calculate the thermodynamic characteristics of their adsorption on graphitic thermal carbon black. The dependences of the thermodynamic functions of S,N-derivatives of alkylhydrazines on their molecular mass and stereochemical features of hydrocarbon radicals have been found. The results have been compared with experimental data obtained by liquid chromatography when studying the interaction of S,N-derivatives of 1,1-dimethylhydrazine with Hypercarb graphitic carbon sorbent.

Unsymmetrical dimethylhydrazine (UDMH) is a rocket propellant for carrier rockets and missiles. UDMH is environmentally hostile compound, which easily forms a variety of toxic products of oxidative transformation. The liquidation of unused UDMH from retired launch sites is performed by the complete burning of UDMH-containing wastes. Due cyclicity of the burning equipment the UDMH-containing wastes are subject of prolonged storage in contact with atmospheric oxygen and thus contains a complicated mixture of UDMH degradation products. High performance liquid chromatography (HPLC), high resolution mass spectrometry (HRMS) and NMR were used for the isolation on characterization of new highly polar and potentially toxic UDMH transformation products in the mixture. Two series of unreported isomers with high ionization cross section in electrospray ionization were isolated by repeated preparative HPLC. The structures of the isomers were established by tandem HRMS and NMR. The cytotoxicity of the isolated compounds has been preliminarily studied and found to be similar to UDMH or higher.

The retention of glycine-based peptides on a Hypercarb carbon adsorbent is studied by reversed-phase HPLC. In the reaction mixture obtained in the synthesis of polyglycines, ions corresponding to both linear and cyclic peptides are detected by electrospray ionization mass spectrometry. Characteristics of the retention of the components are determined on varying the composition of the mobile phase in the isocratic mode. It is shown that some of the detected ions are fragmented ions of linear polyglycines rather than protonated individual cyclic peptides, which complicated identification. The presence of five linear polyglycines as well as cyclodiglycine in the test mixture is confirmed.

A deep convolutional neural network was used for the estimation of gas chromatographic retention indices on non-polar (polydimethylsiloxane and polydimethyl(5%-phenyl) siloxane) stationary phases. The neural network can be used for candidate ranking while searching a mass spectral database. A linear representation (SMILES notation) of the molecule structure was used as an input for the model. The input line was converted to a one-hot matrix and then directly processed by the neural network. The calculation of any common molecular descriptors is avoided, following the modern tendency in machine learning: to allow the neural network to find the most preferable features by itself instead of using hard-coded features. The model has two 1D-convolutional layers with 120 neurons each followed by a pooling layer and a fully-connected layer with 200 hidden neurons. The model was compared with state-of-the-art models for prediction of gas chromatographic indices based on molecular descriptors and on functional groups contributions. On different data sets better accuracy is shown together with greater versatility. The applicability to diverse sets of flavors and fragrances, essential oils, metabolites is shown. The possibility of using the model for improvement of mass spectral identification (without reference retention index) is demonstrated. The median absolute error and the median percentage error are in the range of 17.3 (0.93%) to 38.1 (2.15%) depending on used test data set. Ready-to-use neural network parameters are provided.

This work is devoted to the large-scale statistical evaluation of various aspects of using the retention index for GC-MS library search with a diverse data set. A search in a large library often does not give a correct compound even if a library contains it. One of the methods to improve a spectral library search procedure is to use the retention index information. The aim of this study is to explore some statistical peculiarities which can be helpful for development of automated software which uses a library search of diverse completely unknown compounds in a large database. A data set that was used in this work as a source of queries contains ~11 thousand spectra of compounds which belong to diverse chemical classes. Six equations for matching reference and experimental “retention index – spectrum” pairs were compared. It was found that good results can be obtained when a linear equation for similarity of pairs is used. Similarity of pairs is found as a sum of spectral similarity and of a product of a negative adjustable weight parameter and the absolute difference between reference and query retention indices. This equation performs equal or better than much more complex equations which contain two instead of one adjustable parameters. Widely used threshold-based approach, when candidates with high retention index deviation are rejected, performs worse than other equations. The use of predicted with neural networks retention indices as reference was also considered. Modern universal retention prediction models which are applicable to a wide variety of compounds are still quite inaccurate comparing with values from databases, but these predicted values allow to improve a library search as well. When predicted retention indices are used as reference, the linear equation for matching “retention index – spectrum” pairs also performs equal or better than other equations. The distribution of differences between query indices and reference indices (both calculated and experimental) was found close to exponential distribution near zero. The dependence of a fraction of correct identifications on the reference retention indices accuracy was studied. The addition of random noise with double exponential distribution to exact values was used to create “reference” retention indices with the predefined accuracy. The use of the molecular mass and molecular formula as additional constraints during a library search was also considered.

Porous graphitic carbon is a versatile stationary phase for high-performance liquid chromatography which performs especially well for isomeric separations. Shape-sensitivity of the stationary phase is caused by a steric effect when a molecule interacts with a flat carbon surface. It follows that branched, non-flat molecules are eluted much earlier than flat or linear molecules. In this short communication we show that if a molecule has a highly ionizable group, the “shape” of a molecule part which is farther from the ionizable group affects retention much more than the “shape” of a molecule part which is closer to the ionizable group. Dipeptides which consist of tert-leucine and norleucine were used as an example. Basic and acidic eluents were used. Retention strongly depends on whether a norleucine or tert-leucine residual is located near the non-ionized side in an eluent for both basic and acidic eluents. A residual located on the opposite side is less important. To investigate the possible causes of this peculiar retention behavior we compared the retention behavior of these dipeptides for porous graphitic carbon with the behavior for other types of stationary phases and with the calculated physicochemical properties. Strong and complex dependence of elution order on a mobile phase composition is demonstrated. The separation of other dileucine isomers is also considered. The applicability of porous graphitic carbon for the separation of complex mixtures of isomeric peptides is discussed.