Rapid QSPR model development technique for prediction of vapor pressure of organic compounds

Calculation of the Vapour Pressure of Organic Molecules by Means of a Group-Additivity Method and Their Resultant Gibbs Free Energy and Entropy of Vaporization at 298.15 K

The calculation of the vapour pressure of organic molecules at 298.15 K is presented using a commonly applicable computer algorithm based on the group-additivity method. The basic principle of this method rests on the complete breakdown of the molecules into their constituting atoms, further characterized by their immediate neighbour atoms. The group contributions are calculated by means of a fast Gauss-Seidel fitting algorithm using the experimental data of 2036 molecules from literature. A ten-fold cross-validation procedure has been carried out to test the applicability of this method, which confirmed excellent quality for the prediction of the vapour pressure, expressed in log(pa), with a cross-validated correlation coefficient Q2 of 0.9938 and a standard deviation σ of 0.26. Based on these data, the molecules' standard Gibbs free energy ΔG°vap has been calculated. Furthermore, using their enthalpies of vaporization, predicted by an analogous group-additivity approach published earlier, the standard entropy of vaporization ΔS°vap has been determined and compared with experimental data of 1129 molecules, exhibiting excellent conformance with a correlation coefficient R2 of 0.9598, a standard error σ of 8.14 J/mol/K and a medium absolute deviation of 4.68%.

COSMO-RS Analysis of CO2 Solubility in N-Methyldiethanolamine, Sulfolane, and 1-Butyl-3-methyl-imidazolium Acetate Activated by 2-Methylpiperazine for Postcombustion Carbon Capture

Novel aqueous (aq) blends of N-methyldiethanolamine (MDEA), sulfolane (TMSO₂), and 1-butyl-3-methyl-imidazolium acetate ([bmim][Ac]) with amine activator 2-methylpiperazine (2-MPZ) are analyzed through conductor-like screening model for real solvents (COSMO-RS) for possible application in the chemisorption of CO₂. The molecules associated are analyzed for their ground-state energy, σ potential, and σ surface. Thermodynamic and physicochemical properties have been assessed and paralleled with the experimental data. Vapor pressure of the blended systems and pure component density and viscosity have been compared successfully with the experimental data. Important binary interaction parameters for the aqueous blends over a wide temperature, pressure, and concentration range have been estimated for NRTL, WILSON, and UNIQUAC 4 models. The COSMO-RS theory is further applied in calculating the expected CO₂ solubility over a pressure range of 1.0-3.0 bar and temperature range of 303.15-323.15 K. Henry's constant and free energy of solvation to realize the physical absorption through intermolecular interaction offered by the proposed solvents. Perceptive molecular learning from the behavior of chemical constituents involved indicated that the best suitable solvent is aq (MDEA + 2-MPZ).

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure-activity relationship

Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure-activity relationships (QSARs) for CAAS compounds against Aspergillus niger (A. niger) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models (R² = 0.9346 for A. niger, R² = 0.9590 for P. citrinum,) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.

Application of quantitative structure-property relationship analysis to estimate the vapor pressure of pesticides

The application of molecular descriptors in describing Quantitative Structure Property Relationships (QSPR) for the estimation of vapor pressure (VP) of pesticides is of ongoing interest. In this study, QSPR models were developed using multiple linear regression (MLR) methods to predict the vapor pressure values of 162 pesticides. Several feature selection methods, namely the replacement method (RM), genetic algorithms (GA), stepwise regression (SR) and forward selection (FS), were used to select the most relevant molecular descriptors from a pool of variables. The optimum subset of molecular descriptors was used to build a QSPR model to estimate the vapor pressures of the selected pesticides. The Replacement Method improved the predictive ability of vapor pressures and was more reliable for the feature selection of these selected pesticides. The results provided satisfactory MLR models that had a satisfactory predictive ability, and will be important for predicting vapor pressure values for compounds with unknown values. This study may open new opportunities for designing and developing new pesticide.

Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach

We aim at predicting the effect of structure and isotopic substitutions on the equilibrium vapour pressure isotope effect of various organic compounds (alcohols, acids, alkanes, alkenes and aromatics) at intermediate temperatures. We attempt to explore quantitative structure property relationships by using artificial neural networks (ANN); the multi-layer perceptron (MLP) and compare the performances of it with multi-linear regression (MLR). These approaches are based on the relationship between the molecular structure (organic chain, polar functions, type of functions, type of isotope involved) of the organic compounds, and their equilibrium vapour pressure. A data set of 130 equilibrium vapour pressure isotope effects was used: 112 were used in the training set and the remaining 18 were used for the test/validation dataset. Two sets of descriptors were tested, a set with all the descriptors: number of(12)C, (13)C, (16)O, (18)O, (1)H, (2)H, OH functions, OD functions, CO functions, Connolly Solvent Accessible Surface Area (CSA) and temperature and a reduced set of descriptors. The dependent variable (the output) is the natural logarithm of the ratios of vapour pressures (ln R), expressed as light/heavy as in classical literature. Since the database is rather small, the leave-one-out procedure was used to validate both models. Considering higher determination coefficients and lower error values, it is concluded that the multi-layer perceptron provided better results compared to multi-linear regression. The stepwise regression procedure is a useful tool to reduce the number of descriptors. To our knowledge, a Quantitative Structure Property Relationship (QSPR) approach for isotopic studies is novel.

QSAR study and molecular docking on indirubin inhibitors of Glycogen Synthase Kinase-3

The current study describes the development of in silico models based on a novel alternative of the MTD-PLS methodology (Partial-Least-Squares variant of Minimal Topologic Difference) developed by our group to predict the inhibition of GSK-3β by indirubin derivatives. The new MTD-PLS methodology involves selection rules for the PLS equation coefficients based on physico-chemical considerations aimed at reducing the bias in the output information. These QSAR models have been derived using calculated fragmental descriptors relevant to binding including polarizability, hydrophobicity, hydrogen bond donor, hydrogen bond acceptor, volume and electronic effects. The MTD-PLS methodology afforded moderate but robust statistical characteristics (R2 Y(CUM) = 0.707, Q2(CUM) = 0.664). The MTD-PLS model obtained has been validated in terms of predictive ability by joined internal-external cross-validation applying Golbraikh-Tropsha criteria and Y-randomization test. The information supplied by the MTD-PLS model has been evaluated against Fujita-Ban outcomes that afforded a statistically reliable model (R2=0.923). Furthermore, the results originated from QSAR models were laterally validated with docking insights that suggested the substitution pattern for the design of new indirubins with improved pharmacological potential against GSK-3β. The new restriction rules introduced in this paper are applicable and provide reliable results in accordance with physico-chemical reality.

Prediction of physicochemical properties

Physicochemical properties are key factors in controlling the interactions of xenobiotics with living organisms. Computational approaches to toxicity prediction therefore generally rely to a very large extent on the physicochemical properties of the query compounds. Consequently it is important that reliable in silico methods are available for the rapid calculation of physicochemical properties. The key properties are partition coefficient, aqueous solubility, and pKa and, to a lesser extent, melting point, boiling point, vapor pressure, and Henry's law constant (air-water partition coefficient). The calculation of each of these properties from quantitative structure-property relationships (QSPRs) and from available software is discussed in detail, and recommendations made. Finally, detailed consideration is given of guidelines for the development of QSPRs and QSARs.

Prediction of aqueous solubility, vapor pressure and critical micelle concentration for aquatic partitioning of perfluorinated chemicals

The majority of perfluorinated chemicals (PFCs) are of increasing risk to biota and environment due to their physicochemical stability, wide transport in the environment and difficulty in biodegradation. It is necessary to identify and prioritize these harmful PFCs and to characterize their physicochemical properties that govern the solubility, distribution and fate of these chemicals in an aquatic ecosystem. Therefore, available experimental data (10-35 compounds) of three important properties: aqueous solubility (AqS), vapor pressure (VP) and critical micelle concentration (CMC) on per- and polyfluorinated compounds were collected for quantitative structure-property relationship (QSPR) modeling. Simple and robust models based on theoretical molecular descriptors were developed and externally validated for predictivity. Model predictions on selected PFCs were compared with available experimental data and other published in silico predictions. The structural applicability domains (AD) of the models were verified on a bigger data set of 221 compounds. The predicted properties of the chemicals that are within the AD, are reliable, and they help to reduce the wide data gap that exists. Moreover, the predictions of AqS, VP, and CMC of most common PFCs were evaluated to understand the aquatic partitioning and to derive a relation with the available experimental data of bioconcentration factor (BCF).

A Quantitative Structure-Property Relationship (QSPR) Study of aliphatic alcohols by the method of dividing the molecular structure into substructure

A quantitative structure–property relationship (QSPR) analysis of aliphatic alcohols is presented. Four physicochemical properties were studied: boiling point (BP), n-octanol–water partition coefficient (lg P_OW), water solubility (lg W) and the chromatographic retention indices (RI) on different polar stationary phases. In order to investigate the quantitative structure–property relationship of aliphatic alcohols, the molecular structure ROH is divided into two parts, R and OH to generate structural parameter. It was proposed that the property is affected by three main factors for aliphatic alcohols, alkyl group R, substituted group OH, and interaction between R and OH. On the basis of the polarizability effect index (PEI), previously developed by Cao, the novel molecular polarizability effect index (MPEI) combined with odd-even index (OEI), the sum eigenvalues of bond-connecting matrix (SX_1CH) previously developed in our team, were used to predict the property of aliphatic alcohols. The sets of molecular descriptors were derived directly from the structure of the compounds based on graph theory. QSPR models were generated using only calculated descriptors and multiple linear regression techniques. These QSPR models showed high values of multiple correlation coefficient (R > 0.99) and Fisher-ratio statistics. The leave-one-out cross-validation demonstrated the final models to be statistically significant and reliable.

Thermodynamic and structure-property study of liquid-vapor equilibrium for aroma compounds

Thermodynamic parameters (T, DeltaH degrees , DeltaS degrees , K) were collected from the literature and/or calculated for five esters, four ketones, two aldehydes, and three alcohols, pure compounds and compounds in aqueous solution. Examination of correlations between these parameters and the range values of DeltaH degrees and DeltaS degrees puts forward the key roles of enthalpy for vaporization of pure compounds and of entropy in liquid-vapor equilibrium of compounds in aqueous solution. A structure-property relationship (SPR) study was performed using molecular descriptors on aroma compounds to better understand their vaporization behavior. In addition to the role of polarity for vapor-liquid equilibrium of compounds in aqueous solution, the structure-property study points out the role of chain length and branching, illustrated by the correlation between the connectivity index CHI-V-1 and the difference between T and log K for vaporization of pure compounds and compounds in aqueous solution. Moreover, examination of the esters' enthalpy values allowed a probable conformation adopted by ethyl octanoate in aqueous solution to be proposed.

QSPR modeling of thermal stability of nitroaromatic compounds: DFT vs. AM1 calculated descriptors

The quantitative structure-property relationship (QSPR) methodology was applied to predict the decomposition enthalpies of 22 nitroaromatic compounds, used as indicators of thermal stability. An extended series of descriptors (constitutional, topological, geometrical charge related and quantum chemical) was calculated at two different levels of theory: density functional theory (DFT) and semi-empirical AM1 approaches. Reliable models have been developed for each level, leading to similar correlations between calculated and experimental data (R(2) > 0.98). Hence, both of them can be employed as screening tools for the prediction of thermal stability of nitroaromatic compounds. If using the AM1 model presents the advantage to be less time consuming, DFT allows the calculation of more accurate molecular quantum properties, e.g., conceptual DFT descriptors. In this study, our best QSPR model is based on such descriptors, providing more chemical comprehensive relationships with decomposition reactivity, a particularly complex property for the specific class of nitroaromatic compounds.

APPLICATIONS OF A NEW TOPOLOGICAL INDEX EDm IN SOME ALIPHATIC HYDROCARBONS

A novel index EDm based on ionicity index matrix, improved distance matrix, and branching degree matrix is used to describe the structural information of the molecule and realizes unique characterization of the molecular structures. The quantitative structure–property relationship (QSPR) models, with correlation coefficients (R) in the range of 0.99–1.00 for standard enthalpy of formation ([Formula: see text]), standard entropy ([Formula: see text]), molar volume (V m ), and molar refraction (R m ) of alkanes, alkenes, and alkynes, are subsequently developed using the index EDm. The leave-one-out (LOO) method and random sampling prediction (RSP) method demonstrate the models to be statistically significant and reliable. Compared with other published topological descriptors, the index EDm has many advantages such as zero degeneracy, better simulation, and so on. Furthermore, the models of solubility and octanol–water partition coefficient are built with satisfied results, which further manifests the superiority and wide application of this index.