R. I. Nugmanov

Structure-Reactivity Relationships in Terms of the Condensed Graphs of Reactions

An approach for the prediction of rate constants of chemical reactions, based on the representation of a chemical reaction as a condensed graph, has been tested on more than 1000 bimolecular nucleophilic substitution reactions with neutral nucleophiles in 38 solvents. Molecular fragment descriptors, temperature, and solvent parameters characterizing solvation power have been used in the reaction modeling. The obtained models ensure a good correlation between the predicted and experimental values; the corresponding deviations are comparable with interlaboratory measurement errors.

Structure–reactivity relationship in bimolecular elimination reactions based on the condensed graph of a reaction

By means of a structural representation of the chemical reactivity as a condensed graph a model predicting rate constants of the bimolecular elimination reaction is derived for the first time. The model developed enables the prediction of rate constants of reactions proceeding in different solvents or water-organic mixtures at different temperatures. It demonstrates a good predictive performance: a mean square deviation of predicted values from experimental ones is less than 0.7 logarithmic units. An outlier analysis shows that prediction errors are mainly due to the imperfection of the training data containing unique reactions. The model is available for users at arsole.u-strasbg.fr.

Development of "Structure-Property" Models in Nucleophilic Substitution Reactions Involving Azides

This paper reports a predictive model for the rate constant of the bimolecular nucleophilic substitution involving the azide moiety. It predicts reaction rate constants in different solvents, including organic mixtures, and with different organic and inorganic azides as reactants. The optimal descriptors describing solvent effects and a cation type in the azide salt were suggested. A reasonably good predictive performance of the model in cross-validation has been demonstrated. The model was applied to predict the rates of the reactions between sodium azide with two conformers of calixarenes as well as 3-bromopropyl phenyl ester. For sterically non-hindered molecules, a good agreement between predicted and experimental reaction rates was observed. On the other hand, the model poorly reproduces the results for sterically hindered molecules.

Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis

We report a new method to assess protective groups (PGs) reactivity as a function of reaction conditions (catalyst, solvent) using raw reaction data. It is based on an intuitive similarity principle for chemical reactions: similar reactions proceed under similar conditions. Technically, reaction similarity can be assessed using the Condensed Graph of Reaction (CGR) approach representing an ensemble of reactants and products as a single molecular graph, i.e., as a pseudomolecule for which molecular descriptors or fingerprints can be calculated. CGR-based in-house tools were used to process data for 142,111 catalytic hydrogenation reactions extracted from the Reaxys database. Our results reveal some contradictions with famous Greenes Reactivity Charts based on manual expert analysis. Models developed in this study show high accuracy (ca. 90%) for predicting optimal experimental conditions of protective group deprotection.

Structure–reactivity modeling using mixture-based representation of chemical reactions

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn’t need an explicit labeling of a reaction center. The rigorous “product-out” cross-validation (CV) strategy has been suggested. Unlike the naïve “reaction-out” CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new “mixture” approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Synthesis and structure of lower rim-substituted alkynyl derivatives of thiacalix[4]arene

Lower-rim substituted bis- and tetrakis(alkynyloxy)thiacalix[4]arenes in cone and 1,3-alternate configurations were synthesized by the Mitsunobu reaction, and their structure was determined using homoand heteronuclear one- and two-dimensional NMR techniques. Bis(prop-2-yn-1-yloxy)thiacalix[4]arene was found to exist in conformational equilibrium whose position depends on the temperature and reaction conditions.

Structure–reactivity relationship in Diels–Alder reactions obtained using the condensed reaction graph approach

By the structural representation of a chemical reaction in the form of a condensed graph a model allowing the prediction of rate constants (logk) of Diels–Alder reactions performed in different solvents and at different temperatures is constructed for the first time. The model demonstrates good agreement between the predicted and experimental logk values: the mean squared error is less than 0.75 log units. Erroneous predictions correspond to reactions in which reagents contain rarely occurring structural fragments. The model is available for users at https://cimm.kpfu.ru/predictor/.

Effect of copper(I) on the conformation of the thiacalixarene platform in azide-alkyne cycloaddition

New lower-rim tetrasubstituted p-tert-butylthiacalix[4]arene derivatives bearing alkyl, propargyl, or triazole-containing substituents were synthesized. The structures of these compounds were determined by 1D and 2D NMR spectroscopy in solution and by X-ray diffraction in the solid phase. The copper-catalyzed azide-alkyne cycloaddition (CuAAC) of azides to a mixture of 1,3-alternate–partial cone stereoisomers of dipropargyl derivatives of thiacalix[4]arene affords triazole-containing products exclusively in the 1,3-alternate conformation.

Predictive Models for Kinetic Parameters of Cycloaddition Reactions

This paper reports SVR (Support Vector Regression) and GTM (Generative Topographic Mapping) modeling of three kinetic properties of cycloaddition reactions: rate constant (logk), activation energy (Ea) and pre‐exponential factor (logA). A data set of 1849 reactions, comprising (4+2), (3+2) and (2+2) cycloadditions (CA) were studied in different solvents and at different temperatures. The reactions were encoded by the ISIDA fragment descriptors generated for Condensed Graph of Reaction (CGR). For a given reaction, a CGR condenses structures of all the reactants and products into one single molecular graph, described both by conventional chemical bonds and “dynamical” bonds characterizing chemical transformations. Different scenarios of logk assessment were exploited: direct modeling, application of the Arrhenius equation and temperature‐scaled GTM landscapes. The logk models with optimal cross‐validated statistics (Q2=0.78–0.94 RMSE=0.45–0.86) have been challenged to predict rates for the external test set of 200 reactions, comprising both reactions that were not present in the training set, and training set transformations performed under different reaction conditions. The models are freely available on our web‐server: http://cimm.kpfu.ru/models.

Visualization and Analysis of Complex Reaction Data: The Case of Tautomeric Equilibria

Generative Topographic Mapping (GTM) approach was successfully used to visualize, analyze and model the equilibrium constants (KT) of tautomeric transformations as a function of both structure and experimental conditions. The modeling set contained 695 entries corresponding to 350 unique transformations of 10 tautomeric types, for which KT values were measured in different solvents and at different temperatures. Two types of GTM‐based classification models were trained: first, a “structural” approach focused on separating tautomeric classes, irrespective of reaction conditions, then a “general” approach accounting for both structure and conditions. In both cases, the cross‐validated Balanced Accuracy was close to 1 and the clusters, assembling equilibria of particular classes, were well separated in 2‐dimentional GTM latent space. Data points corresponding to similar transformations measured under different experimental conditions, are well separated on the maps. Additionally, GTM‐driven regression models were found to have their predictive performance dependent on different scenarios of the selection of local fragment descriptors involving special marked atoms (proton donors or acceptors). The application of local descriptors significantly improves the model performance in 5‐fold cross‐validation: RMSE=0.63 and 0.82 logKT units with and without local descriptors, respectively. This trend was as well observed for SVR calculations, performed for the comparison purposes.