A comparative study of the predictive performance of different descriptor calculation tools: Molecular-based elution order modeling and interpretation of retention mechanism for isomeric compounds from METLIN database.

In the pharmaceutical industry, the need for analytical standards is a bottleneck for comprehensive evaluation and quality control of intermediate and end products. These are complex mixtures containing structurally related molecules. In this regard, chromatographic peak annotation, especially for critical pairs of isomers and closest structural analogs, can be supported by using a Quantitative Structure Retention Relationship (QSRR) approach. In our study, we investigated the fundamental basis of the reversed-phase (RP) retention mechanism for 1141 isomeric compounds from the METLIN SMRT dataset. Nine different descriptor calculation tools combined with different feature selection methods (genetic algorithm (GA), stepwise, Boruta) and machine learning (ML) approaches (support vector machine (SVM), multiple linear regression (MLR), random forest (RF), XGBoost) were applied to provide a reliable molecular structure-based interpretation of RP retention behaviour of the isomeric compounds. Strict internal and external validation metrics were used to select models with the best predictive capabilities (rtest > 0.73, order of elution > 60 %). For the developed models, mean absolute errors were in the range of 60 to 110 s. Stepwise and GA showed the most suitable performance as descriptor selection methods, while SVM and XGBoost modeling gave satisfactory predictive characteristics in most cases. Validation performed on the published experimental data for structurally related pharmaceutical compounds confirmed the best accuracy of MLR modeling in combination with GA feature selection of general physico-chemical properties. The resulting models will be useful for the prediction of separation and identification of structurally related compounds in pharmaceutical analysis, providing a simultaneous understanding of the interaction mechanisms leading to their retention under RP conditions.

Thermodynamic Characteristics and Selectivity of the Liquid-Phase Adsorption of Aromatic Compounds on Hypercrosslinked Polystyrene Networks with Ultimate-High Crosslinking Densities by Data of Liquid Chromatography.

This study delves into the thermodynamics of liquid-phase adsorption on hypercrosslinked polystyrene networks (HPSNs), widely recognized for their distinct structure and properties. Despite the considerable progress in HPSN synthesis and characterization, gaps persist regarding the chromatographic retention mechanism, thermodynamics of adsorption, and their impact on the adsorption selectivity, especially in the case of networks with ultra-high crosslinking densities (up to 500%). Utilizing high-performance liquid chromatography (HPLC), we have explored, for the first time, the thermodynamic intricacies of liquid-phase adsorption onto HPSNs crosslinked in the entire range of the crosslinking degree from 100 to 500%. Our findings reveal the dependences of thermodynamic characteristics and selectivity of adsorption on the crosslinking degree, textural features, and liquid-phase composition in the region of extremely low adsorbent surface coverages (Henry's range). We have detected that, in the case of HPSNs, the dependence of the thermodynamic characteristics of adsorption on the liquid-phase composition is different than for classical HPLC stationary phases. Moreover, we scrutinize the impact of the molecular structure of the studied aromatic compounds on the thermodynamic characteristics and selectivity of the liquid-phase adsorption on HPSNs. Investigating liquid-phase adsorption selectivity, we demonstrate the pivotal role of π-π interactions in separating aromatic compounds on HPSNs. Eventually, we unveil that the thermodynamic characteristics of adsorption peculiarly depend on the crosslinking degree due to the profound impact of the crosslinking on the electronic density in benzene rings in HPSNs, whereas the separation throughput peaks for the polymer with a 500% crosslinking degree, attributed to its exceptionally rigid network structure, moderate swelling and micropore volume, and minimum specific surface area.

Intelligent Workflow and Software for Non-Target Analysis of Complex Samples Using a Mixture of Toxic Transformation Products of Unsymmetrical Dimethylhydrazine as an Example.

Unsymmetrical dimethylhydrazine (UDMH) is a widely used rocket propellant. Entering the environment or being stored in uncontrolled conditions, UDMH easily forms an enormous variety (at least many dozens) of transformation products. Environmental pollution by UDMH and its transformation products is a major problem in many countries and across the Arctic region. Unfortunately, previous works often use only electron ionization mass spectrometry with a library search, or they consider only the molecular formula to propose the structures of new products. This is quite an unreliable approach. It was demonstrated that a newly proposed artificial intelligence-based workflow allows for the proposal of structures of UDMH transformation products with a greater degree of certainty. The presented free and open-source software with a convenient graphical user interface facilitates the non-target analysis of industrial samples. It has bundled machine learning models for the prediction of retention indices and mass spectra. A critical analysis of whether a combination of several methods of chromatography and mass spectrometry allows us to elucidate the structure of an unknown UDMH transformation product was provided. It was demonstrated that the use of gas chromatographic retention indices for two stationary phases (polar and non-polar) allows for the rejection of false candidates in many cases when only one retention index is not enough. The structures of five previously unknown UDMH transformation products were proposed, and four previously proposed structures were refined.

Diazines on graphene: adsorption, structural variances and electronic states.

We conduct quantum studies of adsorption of diazine heterocycles on graphene to discuss experimental thermodynamics of gas-phase adsorption of pyridazine, pyrimidine and pyrazine on graphitized thermal carbon black, as reported previously. Using Born-Oppenheimer molecular dynamics and density functional studies, we characterize structural and electronic tendencies of the heterocycles on graphene. The theoretical studies predict the adsorption of pyridazine, pyrazine and pyrimidine to cause electronic perturbations of dipole, quadrupole and mixed spatial characters, respectively, resulting in a red shift of the electronic components of the heterocycles to modulate graphene electronics upon admixing of diazine orbital components with the πz states of the substrate. Investigating the thermodynamics of adsorption further involves calculating Henry's constant with the expression of the uniform surface limit: using experimental data, we estimate binding energies and force derivatives with respect to the surface normal. The extracted association energies agree with the results of Lennard-Jones potential calculations. Together, the reported pyridazine anomalous retention required the association force constant to be lower compared with values for the other diazines. Exploring energies of intermolecular relations, we ascribe the pyridazine anomalous retention to possibility of the formation of pyridazine dimers: when on the surface, only for pyridazine, the computed benefit of pairing is larger than the energy of molecular association with graphene.

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification.

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.

Machine learning-assisted non-target analysis of a highly complex mixture of possible toxic unsymmetrical dimethylhydrazine transformation products with chromatography-mass spectrometry.

Unsymmetrical dimethylhydrazine (UDMH) is a toxic and environmentally hostile compound that was massively introduced to the environment during previous decades due to its use in the space and rocket industry. The compound forms multiple transformation products, and many of them are as dangerous as UDMH or even more dangerous. The danger includes, but is not limited to, acute toxicity, chronic health hazards, carcinogenicity, and environmental damage. UDMH transformation products are poorly investigated. In this work, the mixture formed by long storage of the waste that contained UDMH was studied. Even a preliminary screening of such a mixture is a complex task. It consists of dozens of compounds, and most of them are missing in chemical and spectral databases. The complete preparative separation of such a mixture is very laborious. We applied several methods of gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry, and several machine learning and chemoinformatics methods to make a preliminary but informative screening of the mixture. Machine learning allowed predicting retention indices and mass spectra of candidate structures. The combination of various ion sources and a comparison of the observed with the predicted spectra and retention was used to propose confident structures for 24 compounds. It was demonstrated that neither high-resolution mass spectrometry nor mass spectral library matching is enough to elucidate the structures of unknown UDMH transformation products. At the same time, the use of machine learning and a combination of methods significantly improves the identification power. Finally, machine learning was applied to estimate the acute toxicity of the discovered compounds. It was shown that many of them are comparable to or even more toxic than UDMH itself. Such an extremely wide and still underestimated variety of easily formed derivatives of UDMH can lead to a significant underestimation of the potential hazard of this compound.

Understanding the Working Mechanism of the Novel HKUST-1@BPS Composite Materials as Stationary Phases for Liquid Chromatography.

Composite materials have been used based on coordination polymers or microporous metal-organic frameworks (MOFs) combined with mesoporous matrices for adsorption-related techniques, which enable outflanking some adverse phenomena manifested during pristine components operation and enhance the performance and selectivity of the resulting materials. In this work, for the first time, the novel HKUST-1@BPS composites synthesized by the microwave-assisted (MW) technique starting from microporous HKUST-1 (Cu3(btc)2) MOF and biporous silica matrix (BPS) with bimodal mesopore size distribution were comparatively studied as materials for liquid-phase adsorption techniques utilizing the high-performance liquid chromatography (HPLC) method and benzene as a model adsorbate. It was established that the studied HKUST-1@BPS composites can function as stationary phases for HPLC, unlike the pristine HKUST-1 and bare BPS materials, due to the synergetic effect of both components based on the preliminary enhanced adsorbate mass transfer throughout the silica mesopores and, subsequently, its penetrating into HKUST-1 micropores. The suggested mechanism involves the initial deactivation of open metal Cu2+ sites in the HKUST-1 framework structure by isopropanol molecules upon adding this polar component into the mobile phase in the region of the isopropanol concentration of 0.0 to 0.2 vol.%. Thereafter, at the medium range of varying the isopropanol concentration in the eluent of 0.2 to 0.3 vol.%, there is an expansion of the previously inaccessible adsorption centers in the HKUST-1@BPS composites. Subsequently, while further increasing the isopropanol volume fraction in the eluent in the region of 0.3 to 5.0 vol.%, the observed behavior of the studied chromatographic systems is similar to the quasi-normal-phase HPLC pattern. According to the obtained thermodynamic data, benzene adsorption into HKUST-1 micropores from solutions with a vol.% of isopropanol in the range of 0.4 to 5.0 follows the unique entropy-driven mechanism previously described for the MIL-53(Al) framework. It was found that HKUST-1 loading in the composites and their preparation conditions have pronounced effects on their physicochemical properties and adsorption performance, including the adsorption mechanism.

Direct C-H borylation of vinylporphyrins via copper catalysis.

A method of direct borylation of vinyl-substituted porphyrinoids (porphyrins and chlorins) has been developed based on the copper catalyzed vinylic C-H activation. Ni(II) complexes of meso- and ß-vinylporphyrinoids have been transformed to the corresponding pinacolboronated derivatives with good yields and high (E)-stereoselectivity. The method provides an easy and direct access to the valuable synthons which were shown to act as nucleophylic partners in the Suzuki cross-coupling building tetrapyrrole derivatives with π-conjugation through the carbon-carbon double bond.

Deep learning for retention time prediction in reversed-phase liquid chromatography.

Retention time prediction in high-performance liquid chromatography (HPLC) is the subject of many studies since it can improve the identification of unknown molecules in untargeted profiling using HPLC coupled with high-resolution mass spectrometry. Lots of approaches were developed for retention time prediction in liquid chromatography for a different number of molecules considering various molecular properties and machine learning algorithms. The recently built large retention time data set of standard compounds from the Metabolite and Chemical Entity Database (METLIN) allows researchers to create a model that can be used for retention time prediction of small molecules with wide varieties of structures and physicochemical properties. The ability to predict retention times using the largest data set was studied for different architectures of deep learning models that were trained on molecular fingerprints, and SMILES (string representation of a molecule) represented as one-hot matrices. The best result was achieved with a one-dimensional convolutional neural network (1D CNN) that uses SMILES as an input. The proposed model reached the mean absolute error and the median absolute error equal to 34.7 and 18.7 s, respectively, which outperformed the results previously obtained for this data set. The pre-trained 1D CNN on the METLIN SMRT data set was transferred on five other data sets to evaluate the generalization ability.

A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry.

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.

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.

Aged diesel and heavy metal pollution in the Arctic tundra (Yamal Peninsula, Russia).

Monitoring pollution in Arctic regions is a challenging and important task, regardless of the way these lands are used. The summer 2019 expedition to the Yamal Peninsula revealed historic petroleum pollution of the tundra area adjacent to "Yamalsky" natural reserve. Soil, surface water and bottom sediments from a downhill lake, and herbaceous plant Eriophorum scheuchzeri samples were collected to address the origin and the level of the aged pollution, and to investigate, if E. scheuchzeri species could be a potential phytoremediation agent. Compositional GC-MS analysis of the soil organic matter showed that diesel fuel spillage affected the study area and the territories nearby. Weathered diesel compounds penetrated the soil and reached the permafrost layer at 85 cm depth. Petroleum hydrocarbon level peaked at 11% (wt) in the topsoil at the polluted site and 3% (wt) in the bottom sediments of the downhill lake, demonstrating chronic ecosystem exposure. The following ICP-MS analysis showed presence of trace elements (V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Mo, Ag, Cd, Ba, Pb, Bi, U) in the soil, water, and E. scheuchzeri samples. Observed concentrations of V, Cr, Cd, Pb, Ni, and Zn in the soil samples exceeded the background values by 3.6, 2.3, 9.7, 2.9, and 3.0 times, respectively. V (0.4 mg/L) and Cr (0.12 mg/L) levels in the lake water exceeded the established national limits by 40 and 2.4 times, respectively, which demonstrated the possibility of pollution migration with groundwater or surface water. The plant E. scheuchzeri tolerated diesel pollution and stimulated natural attenuation, bioaccumulating Mo, Cd, Ba, and Bi in its tissue from the soil. E. scheuchzeri is proposed for phytoremediation of Arctic soils polluted with petroleum and metals.

Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics.

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.

Analysis of light components in pyrolysis products by comprehensive two-dimensional gas chromatography with PLOT columns.

The most successful method for pyrolysis liquids analysis is comprehensive two-dimensional gas chromatography. Columns with a stationary liquid phase are used for this purpose. However, when is necessary to analyze a gas phase containing C3-C5 hydrocarbons over a liquid pyrolysis product, the use of columns with a liquid phase in CG*CG will not result to separation of light hydrocarbons. In this case, it is necessary to use PLOT columns with a porous layer of sorbents of various nature. Today this approach with two PLOT columns in GC*GC is not described, as well as its use for the analysis of light hydrocarbons resulting from pyrolysis. This paper describes an application of two PLOT columns in GC*GC mode. This paper describes an application of two PLOT columns in GC*GC mode. The next columns of different nature that have different selectivity were used: Rt-Q-BOND, Rt-S-BOND, Rt-U-BOND (columns based on divinylbenzene styrene copolymer), column with sorbent poly- (1-trimethylsilyl-1-propyne) (PTMSP) and an Agilent GASPRO silica column. The most suitable pair of the columns was determined by finding of their orthogonality. The numerical orthogonality data was found by studying of the correlation coefficients between compounds retention time on the first and second columns. It is shown that the best combination of columns are PTMSP - GASPRO and Rt-Q-BOND - GASPRO, however, the first combination of columns allows separation at the same temperature conditions about twice as fast as the second. Examples of the separation of С3-С8 hydrocarbons in the gas phase over pyrolysis mixtures of different origin are given.

A peculiar chromatographic selectivity of porous graphitic carbon during the separation of dileucine isomers.

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.

A deep convolutional neural network for the estimation of gas chromatographic retention indices.

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.

Isomeric derivatives of triazoles as new toxic decomposition products of 1,1-dimethylhydrazine.

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.