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Ebola and Marburg viruses, biosafety level 4 pathogens, cause severe hemorrhaging and organ failure with high mortality. Although some FDA-approved vaccines or therapeutics like Ervebo for Zaire Ebola virus exist, still there is a lack of effective therapeutics that cover all filoviruses, including both Ebola and Marburg viruses. Therefore, some anti-filovirus drugs such as Pinocembrin, Favipiravir, Remdesivir and others are used to manage infections. In theoretical chemistry, a chemical molecule is converted into an isomorphic molecular graph, G (V,E) by considering atom set V as vertices and bond set E as edges. A topological index is a molecular descriptor derived from the molecular graph of a chemical compound that characterizes its topology. The relationship between a compound’s chemical structure and its properties is investigated through the quantitative structure-property relationship (QSPR). This article introduces new reverse sum Revan degree based indices to explore the physicochemical and pharmacokinetic properties of anti-filovirus drugs via multilinear regression. The findings reveal a strong correlation between these proposed indices and the properties of anti-filovirus drugs when compared to reverse and Revan degree-based indices. Thus, reverse sum Revan indices offer valuable insights for analyzing the drugs properties used to treat Ebola and Marburg virus infections. Moreover, the multilinear regression (MLR) results through reverse sum Revan indices are compared with Artificial Neural Network (ANN) modelling technique and it provides the better prediction of the physicochemical and pharmacokinetic properties of anti-filovirus drugs.

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R2) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), and 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] quinoxaline (Q6), show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

Abstract This study describes the development and evaluation of six new models for predicting physical–chemical (PC) properties that are highly relevant for chemical hazard, exposure, and risk estimation: solubility (in water S W and octanol S O ), vapor pressure (VP), and the octanol–water (K OW ), octanol–air (K OA ), and air–water (K AW ) partition ratios. The models are implemented in the Iterative Fragment Selection Quantitative Structure–Activity Relationship (IFSQSAR) python package, Version 1.1.0. These models are implemented as Poly-Parameter Linear Free Energy Relationship (PPLFER) equations which combine experimentally calibrated system parameters and solute descriptors predicted with QSPRs. Two other ancillary models have been developed and implemented, a QSPR for Molar Volume (MV) and a classifier for the physical state of chemicals at room temperature. The IFSQSAR methods for characterizing applicability domain (AD) and calculating uncertainty estimates expressed as 95% prediction intervals (PI) for predicted properties are described and tested on 9,000 measured partition ratios and 4,000 VP and S W values. The measured data are external to IFSQSAR training and validation datasets and are used to assess the predictivity of the models for “novel chemicals” in an unbiased manner. The 95% PI intervals calculated from validation datasets for partition ratios needed to be scaled by a factor of 1.25 to capture 95% of the external data. Predictions for VP and S W are more uncertain, primarily due to the challenges in differentiating their physical state (i.e., liquids or solids) at room temperature. The prediction accuracy of the models for log K OW , log K AW and log K OA of novel, data-poor chemicals is estimated to be in the range of 0.7 to 1.4 root mean squared error of prediction (RMSEP), with RMSEP in the range 1.7–1.8 for log VP and log S W . Scientific contribution New partitioning models integrate empirical PPLFER equations and QSARs, allowing for seamless integration of experimental data and model predictions. This work tests the real predictivity of the models for novel chemicals which are not in the model training or external validation datasets. Graphical Abstract

This study investigates the quantitative structure–property relationship (QSPR) modeling of guar gum biomolecules, focusing on their structural parameters. Guar gum, a polysaccharide with diverse industrial applications, exhibits various properties such as viscosity, solubility, and emulsifying ability, which are influenced by its molecular structure. In this research, M-polynomial and associated topological indices are employed as structural descriptors to represent the molecular structure of guar gum. The M-polynomial and associated topological indices capture important structural features, including size, shape, branching, and connectivity. By correlating these descriptors with experimental data on guar gum properties, predictive models are developed using regression analysis techniques. The analysis revealed a strong correlation between the boiling point and molecular weight and all the considered topological descriptors. The resulting models offer insights into the relationship between guar gum structure and its properties, facilitating the optimization of guar gum production and application in various industries. This study demonstrates the utility of M-polynomial and QSPR modeling in elucidating structure–property relationships of complex biomolecules like guar gum, contributing to the advancement of biomaterial science and industrial applications.

Biogas is a renewable energy source and needs to be upgraded to biomethane for injection into the natural gas grid or use as fuel. To design ionic liquid (IL) solvents for biogas upgrading, a computer-aided ionic liquid design (CAILD) method and the corresponding process simulation are presented. The UNIFAC-IL model is employed to calculate the solubility of gases in ILs, while group contribution (GC) based models are used to predict the physicochemical properties of ILs. By using the performance index (PI) as the objective function and the structural feasibility and physicochemical properties as constraints, a mixed-integer nonlinear programming (MINLP) problem is formulated and solved by the generate-and-test method. Two IL solvents, [MMPY][Tf2N] (1,3-dimethylpyridinium bis(trifluoromethylsulfonyl)imide) and [MMPY][eFAP] (1,3-dimethylpyridinium tris(pentafluoroethyl) trifluorophosphate), are found to be the optimal IL solvents from 880 IL candidates. To perform the process simulation by using the designed ILs, the parameters for the equations of calculation of required properties are regressed. A sensitivity analysis is performed to find the optimal conditions for the process. Finally, the developed process is compared with the water-scrubbing process for biogas upgrading.

Abstract Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure–property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies. Graphical Abstract

This scientific paper aims to investigate the best machine learning (ML) for predicting the corrosion inhibition efficiency (CIE) value of amino acid compounds. The study applied a quantitative structure–property relationship (QSPR) model based on an ML approach to predict the CIE values of three new amino acid compounds, namely L-asparagine (LA), L-isoleucine (LI), and L-proline (LP). The result is that the Gradient Boosting Regressor (GBR) model is proven to be the best predictive model based on the coefficient of determination (R2) and root mean square error (RMSE) metrics used. The study found that the three amino acid compounds LA, LI, and LP tested had high CIE values, ranging from 90.49% to 93.67%. These results are also relevant to the CIE values resulting from experimental studies and show a trend that is by the adsorption energy trend. This engineering breakthrough can be used to predict the corrosion inhibition properties of new compounds before experimental synthesis.

Quantitative structure-property relationship (QSPR) is a powerful analytical method to find correlations between the structure of a molecule and its physicochemical properties. The glass transition temperature (Tg) is one of the most reported properties, and its characterisation is critical for tuning the physical properties of materials. In this work, we explore the use of machine learning in the field of QSPR by developing a recurrent neural network (RNN) that relates the chemical structure and the glass transition temperature of molecular glass formers. In addition, we performed a chemical embedding from the last hidden layer of the RNN architecture into an m-dimensional Tg-oriented space. Then, we test the model to predict the glass transition temperature of essential amino acids and peptides. The results are very promising and they can open the door for exploring and designing new materials.

Abstract Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compound solubility in organic solvents. A dataset containing 5081 experimental temperature and solubility data of compounds in organic solvents was extracted and standardized. Molecular fingerprints were selected to characterize structural features. lightGBM was compared with deep learning and traditional machine learning (PLS, Ridge regression, kNN, DT, ET, RF, SVM) to develop models for predicting solubility in organic solvents at different temperatures. Compared to other models, lightGBM exhibited significantly better overall generalization (logS ± 0.20). For unseen solutes, our model gave a prediction accuracy (logS ± 0.59) close to the expected noise level of experimental solubility data. lightGBM revealed the physicochemical relationship between solubility and structural features. Our method enables rapid solvent screening in chemistry and may be applied to solubility prediction in other solvents.

Glass transition temperature and related dynamics play an essential role in amorphous materials research since many of their properties and functionalities depend on molecular mobility. However, the temperature dependence of the structural relaxation time for a given glass former is only experimentally accessible after synthesizing it, implying a time-consuming and costly process. In this work, we propose combining artificial neural networks and disordered systems theory to estimate the glass transition temperature and the temperature dependence of the main relaxation time based on the knowledge of the molecule's chemical structure. This approach provides a way to assess the dynamics of molecular glass formers, with reasonable accuracy, even before synthesizing them. We expect this methodology to boost industrial development, save time and resources, and accelerate the scientific understanding of structure-properties relationships.

Inhibition corrosion of metals by using organic compounds has become an unavoidable means. So, in this work, the effect of methylxanthines on copper corrosion inhibition in 1M HNO3 was investigated by mass loss measurements and by two theoretical approaches: Density Functional Theory (DFT) and Quantitative Structure-Property Relationship (QSPR.) Quantum chemical calculations based on DFT at the B3LYP/6-31G(d) level permit to establish a correlation between the quantum chemical parameters and the experimental inhibition efficiency (IE %). It was found that inhibition efficiencies increase with increasing temperature and immersion time. In addition, the QSPR approach was used to find the best set of parameters for each molecule. This set of parameters make it possible to characterize the inhibition performance of the tested molecules solution significantly. The theoretical calculations are consistent with the experimental results.

The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.

Flashpoint is one of the most important flammability characteristics of chemical compounds. In the present study, we developed a neural network model for accurate prediction of the flashpoint of chemical compounds, using the number of hydrogen and carbon atoms, critical temperature, normal boiling point, acentric factor, and enthalpy of formation as model inputs. Using a robust strategy to efficiently assign neural network parameters and evaluate the authentic performance of the neural networks, we could achieve an accurate model that yielded average absolute relative errors of 0. 97, 0. 96, 0.99 and 1.0% and correlation coefficients of 0.9984, 0.9985, 0.9981 and 0.9979 for the overall, training, validation and test sets, respectively. These results are among the most accurate ever reported ones, to date.

Quantitative Structure-Properties Relationship (QSPR) and molecular dynamics simulations studies were carried out on the 53 lubricating oil additives and hydrogen-containing DLC (a-C: H). Good QSPR model was developed along with squared correlation coefficient (R2), adjusted squared correlation coefficient (R2adj), leave one out cross-validation coefficient (Q2) and the external validation (R2ext) of values 0.807208, 0.763674, 0.68867 and 0.6297 respectively which shows that model I was reliable and satisfactory. Molecular dynamics simulation binding energy calculations between the lubricant additives and the hydrogen-containing DLC (a-C: H) crystals surface revealed that the best molecular dynamic binding energy was found to be -2112.06 kcal/mol and was found to be better than the one reported by other researchers. Moreover, the lubricant additive with compound number 50** (S-(2-(benzo[d]thiazol-2-ylamino)-2-oxoethyl) O-hexyl carbonodithioate) was found to have the best molecular dynamic binding energy of -2112.06 kcal/mol which conformed with excellent best-normalized onset temperature (Tonset) 2.467K of the same compound. Moreover, Table 7 revealed that the time(s) used for every simulation varies from 12683.13s to 138841.09s for all the studied additives. This investigation will help in rational additive design and synthesis of new and better lubricant additives with predetermined promising binding energy and onset temperature (Tonset) and will provide valuable information for the understanding of dynamic binding energy between DLC substrate and the new compounds and will give the way toward the discovery of novel lubricating oil additives that can withstand high dynamic working temperature and also resist wearing and frictions.

Abstract Ensemble learning helps improve machine learning results by combining several models and allows the production of better predictive performance compared to a single model. It also benefits and accelerates the researches in quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR). With the growing number of ensemble learning models such as random forest, the effectiveness of QSAR/QSPR will be limited by the machine’s inability to interpret the predictions to researchers. In fact, many implementations of ensemble learning models are able to quantify the overall magnitude of each feature. For example, feature importance allows us to assess the relative importance of features and to interpret the predictions. However, different ensemble learning methods or implementations may lead to different feature selections for interpretation. In this paper, we compared the predictability and interpretability of four typical well-established ensemble learning models (Random forest, extreme randomized trees, adaptive boosting and gradient boosting) for regression and binary classification modeling tasks. Then, the blending methods were built by summarizing four different ensemble learning methods. The blending method led to better performance and a unification interpretation by summarizing individual predictions from different learning models. The important features of two case studies which gave us some valuable information to compound properties were discussed in detail in this report. QSPR modeling with interpretable machine learning techniques can move the chemical design forward to work more efficiently, confirm hypothesis and establish knowledge for better results.

One of the main disadvantages of the use of synthetic or semi-synthetic polymeric materials is their degradation and aging. The purpose of this study was to use artificial neural networks (ANN) and multiple linear regressions (MLR) to predict the carbonyl, hydroxyl, and polyene indices (ICO, IOH, and IOP), and viscosity average molecular weight (MV) of poly(vinyl chloride), polystyrene, and poly(methyl methacrylate). These physicochemical properties are considered fundamental during the study of photostabilization of polymers. From the five repeating units of monomers, the structure of the polymer studied is shown. Quantitative structure-property relationship (QSPR) models obtained by using relevant descriptors showed good predictability. Internal validation {R2, RMSE, and Q2LOO}, external validation {R2, RMSE, Q2pred, rm2, Δrm2, k, and k’}, and applicability domain were used to validate these models. The comparison of the results shows that the ANN models are more efficient than those of the MLR models. Accordingly, the QSPR model developed in this study provides excellent predictions, and can be used to predict ICO, IOH, IOP, and MV of polymers, particularly for those that have not been tested.

Quantitative structure–solubility relationships (QSSR) are considered as a type of Quantitative structure–property relationship (QSPR) study in which aqueous solubility of chemicals are related to chemical structure. In the present work, multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for QSSR studies of the water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and a genetic algorithm (GA), the descriptors that resulted in the best fitted models were selected. After descriptor selection, multiple linear regression (MLR) was used to construct a linear QSSR model. The R2 = 91.0 %, LOO Q2 = 89.33 %, s = 0.340 values of the model developed by MLR showed a good predictive capability for log S values of phenol and its derivatives. The results of MLR model were compared with those of the ANN model. the comparison showed that the R2 = 94.99 %, s = 0.245 of ANN were higher and lower, respectively, which illustrated an ANN presents an excellent alternative to develop a QSSR model for the log S values of phenols to MLR.

Abstract Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. Various molecular-descriptor-calculation software programs have been developed. However, users of those programs must contend with several issues, including software bugs, insufficient update frequencies, and software licensing constraints. To address these issues, we propose Mordred, a developed descriptor-calculation software application that can calculate more than 1800 two- and three-dimensional descriptors. It is freely available via GitHub. Mordred can be easily installed and used in the command line interface, as a web application, or as a high-flexibility Python package on all major platforms (Windows, Linux, and macOS). Performance benchmark results show that Mordred is at least twice as fast as the well-known PaDEL-Descriptor and it can calculate descriptors for large molecules, which cannot be accomplished by other software. Owing to its good performance, convenience, number of descriptors, and a lax licensing constraint, Mordred is a promising choice of molecular descriptor calculation software that can be utilized for cheminformatics studies, such as those on quantitative structure–property relationships.

This study describes the development of a quantitative structure–property relationship to predict the retention indices of volatile and semi-volatile compounds identified in Arabica coffee samples from different geographical origins. The analytical method utilized rapid headspace solid-phase microextraction (HSSPME)–gas chromatography–time-of-flight mass spectrometry (GC-TOFMS) data measured in divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber. A total of 102 molecules were optimized with the PM6/ZDO level of theory in order to calculate several molecular descriptors. Ordinary least squares were coupled with genetic algorithm–supervised variable subset selection to find the best three descriptors. For model validation, the dataset was split into a training set (70%) and a test set (30%). The quality of the model was evaluated by means of the coefficient of determination and the root-mean-square error.

Unsaturated polyester resins are widely used for the preparation of composite materials and fulfill the majority of practical requirements for industrial and domestic applications at low cost. These resins consist of a highly viscous polyester oligomer and a reactive diluent, which allows its process ability and its crosslinking. The viscosity of the initial polyester and the reactive diluent mixture is critical for practical applications. So far, these viscosities were determined by trial and error which implies a time-consuming succession of manipulations, to achieve the targeted viscosities. In this work, we developed a strategy for predicting the viscosities of unsaturated polyesters formulation based on neural networks. In a first step 15 unsaturated polyesters have been synthesized through high-temperature polycondensation using usual monomers. Experimental Hansen solubility parameters (HSP) were determined from solubility experiment with HSPiP software and glass transition temperatures (Tg) were measured by Differential Scanning Calorimetry (DSC). Quantitative Structure—Property Relationship (QSPR) coupled to multiple linear regressions have been used to get a prediction of Hansen solubility parameters δd, δp, and δh from structural composition. A second QSPR regression has been done on glass transition temperature (prediction vs. experimental coefficient of determination R2 = 0.93) of these unsaturated polyesters. These unsaturated polyesters were next diluted in several solvents with different natures (ethers, esters, alcohol, aromatics for example) at different concentrations. Viscosities at room temperature of these polyesters in solution were finally measured in order to create a database of 220 entries with 7 descriptors (polyester molecular weight, Tg, dispersity index Ð, polyester-solvent HSP RED, molar volume of the solvent, δh of the solvent, concentration of polyester in solvent). The QSPR method for predicting the viscosity from these 6 descriptors proved to be ineffective (R2 = 0.56) as viscosities exhibit non-linear phenomena. A Neural Network with an optimized number of 12 hidden neurons has been trained with 179 entries to predict the viscosity. A correlation between experimental and predicted viscosities based on 41 testing instances gave a correlation coefficient R2 of 0.88 and a predicted vs. measured slope of 0.98. Thanks to Neural Networks, new developments with eco-friendly reactive diluents can be accelerated.

The quantitative structure property relationship (QSPR) for the boiling point (Tb) of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) was investigated. The molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and Tb was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN), respectively. Leave-one-out cross validation and external validation were carried out to assess the prediction performance of the models developed. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and external validation was 1.77 and 1.23, respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and external validation was 1.65 and 1.16, respectively. A quantitative relationship between the MDEV index and Tb of PCDD/Fs was demonstrated. Both MLR and ANN are practicable for modeling this relationship. The MLR model and ANN model developed can be used to predict the Tb of PCDD/Fs. Thus, the Tb of each PCDD/F was predicted by the developed models.

The quantitative structure property relationship (QSPR) for supercooled liquid vapour pressures (PL) of PBDEs was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and lgPL was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave-one-out cross validation and k-fold cross validation were carried out to assess the prediction ability of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and k-fold cross validation is 9.95 and 9.05 respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and k-fold cross validation is 8.75 and 8.31 respectively. It is demonstrated the established models are practicable for predicting the lgPL of PBDEs. The MDEV index is quantitatively related to the lgPL of PBDEs. MLR and L-ANN are practicable for modeling this relationship. Compared with MLR, ANN shows slightly higher prediction accuracy. Subsequently, an MLR model, which regression equation is lgPL = 0.2868 M11 - 0.8449 M12 - 0.0605, and an ANN model, which is a two inputs linear network, were developed. The two models can be used to predict the lgPL of each PBDE.

Study of the adsorption and electroreduction behavior of cefpodoxime proxetil, cefotaxime, desacetylcefotaxime, cefetamet, ceftriaxone, ceftazidime, and cefuroxime axetile at the mercury electrode surface has been performed using Cyclic (CV), Differential Pulse (DPV), and Adsorptive Stripping Differential Pulse Voltammetry (AdSDPV). The Quantitative Structure Property Relationship (QSPR) study of the seven cephalosporins adsorption at the mercury electrode has been based on the density functional theory DFT-B3LYP/6-31G (d,p) calculations of molecular orbitals, partial charges and electron densities of analytes. The DFT-parameters and QSPR model explain well the process of adsorption of the examined cephalosporins. QSPR study defined that cefalosporins with lower charge of sulphur in the thiazine moiety, lower electron density on the nitrogen atom of the N-O bond, higher number of hydrogen bond accepting groups, and higher principal moment of inertia should express high adsorption on the mercury electrode. [Projekat Ministarstva nauke Republike Srbije, br. 172033]

A quantitative structure - property relationship (QSPR) modeling of vapor pressure at 298.15 K, expressed as log (VP / Pa) was performed for a series of 84 hydrocarbons (63 alkanes and 21 cycloalkanes) using the van der Waals (vdW) surface area, SW/Å2, calculated by the Monte Carlo method, as the molecular descriptor. The QSPR model developed from the subset of 63 alkanes (C1-C16), deemed as the training set, was successfully used for the prediction of the log (VP / Pa) values of the 21 cycloalkanes, which was the external prediction (test) subset. A QSPR model was also developed for a series composed of all 84 hydrocarbons. Both QSPR models were statistically tested for their ability to fit the data and for prediction. The results showed that the vdW molecular surface used as molecular descriptor (MD) explains the variance of the majority of the log (VP / Pa) values in this series of 84 hydrocarbons. This MD describes very well the intermolecular forces that hold neutral molecules together. The clear physical meaning of the molecular surface values, SW/Å2, could explain the success of the QSPR models obtained with a single structural molecular descriptor.

2D-QSPR models were developed in order to predict aqueous solubility of organic compounds at different temperatures. Impact of various structure features was analyzed and benzene residues contribution to solubility was defined. A reliable model which allows to assess temperature influence on forming compounds solubility was developed.

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.

The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

The main objective of the present study was to develop quantitative-structure property relationship (QSPR) models with predictability for release, transportability and related properties of formulation containing a neutral drug (glimepiride) based on polymeric properties. Such physicochemical properties are measure of polymers’ behavior in deciding release, transportability and bioavailability of drug. Therefore comprehensive study of properties could help in deciding proper polymer composite for formulation with required characteristics. A total of nine glimepiride (GLMP) tablet batches were prepared using three polymers representing extended, moderate and immediate release categories. Molecular descriptors were calculated from polymeric structures and correlated with formulation characteristics. This leads to generation of predictive models. Compatibility between drug-excipients was confirmed. Weight uniformity, drug content, hardness and friability tests showed acceptable results. In vitro dissolution kinetics exhibited Korsmeyer-peppas model as best fit. Best correlation coefficient and validation of developed QSPRs showed powerful predictability for properties. Transportability was influenced by release rate together with molecular size, pKa, log P and water solubility of GLMP. Generated models were found to significantly predict the release, transportability and related formulation properties. QSPR models were developed with enough prediction potential for the properties of formulation containing any neutral drug and could also help to decide the formulation composition for required characteristics together with pharmacoeconomic impact with respect to time, cost and material.

Quantitative structure–property relationship (QSPR) studies based on ant colony optimization (ACO) were carried out for the prediction of λmax of 9,10-anthraquinone derivatives. ACO is a meta-heuristic algorithm, which is derived from the observation of real ants and proposed to feature selection. After optimization of 3D geometry of structures by the semi-empirical quantum-chemical calculation at AM1 level, different descriptors were calculated by the HyperChem and Dragon softwares (1514 descriptors). A major problem of QSPR is the high dimensionality of the descriptor space; therefore, descriptor selection is the most important step. In this paper, an ACO algorithm was used to select the best descriptors. Then selected descriptors were applied for model development using multiple linear regression. The average absolute relative deviation and correlation coefficient for the calibration set were obtained as 3.3% and 0.9591, respectively, while the average absolute relative deviation and correlation coefficient for the prediction set were obtained as 5.0% and 0.9526, respectively. The results showed that the applied procedure is suitable for prediction of λmax of 9,10-anthraquinone derivatives.

Blending an optimum amount of friction modifiers into lubricant is one of the important measures to reduce fuel consumption induced by the frictional loss for automobiles. However, the agents containing metal or phosphorus compounds can cause catalyst poisoning and clogging in the filters of the exhaust gas refining system. Thus, development of metal-free and phosphorus-free agents with a long-term stability under real working condition is highly desirable. In the present work, the friction coefficients on the metal surface in lubricants containing different friction modifiers were investigated by the reciprocating friction test in detail. A statistical method based on the quantitative structure-property relationship (QSPR) analysis has been employed to correlate the chemical nature of additives with their effect on the friction reduction behaviors. An empirical equation relating the molecular structure and its friction reduction efficiency has been proposed as a standard for a good friction modifier. Furthermore, a surface-sensitive vibration spectroscopy, sum frequency generation (SFG) spectroscopy, has been used to analyze the molecular structures of the lubricants adsorbed on the metal surface. The SFG observation suggested that a certain relationship between the adsorption structure and friction reduction effect of these agents.

The mean molecular connectivity indices (MMCI) proposed in previous studies are used in conjunction with well-known molecular connectivity indices (MCI) to model eleven properties of organic solvents. The MMCI and MCI descriptors selected by the stepwise multilinear least-squares (MLS) procedure were used to perform artificial neural network (ANN) computations, with the aim of detecting the advantages and limits of the ANN approach. The MLS procedure can replicate the obtained results for as long as is needed, a characteristic not shared by the ANN methodology, which, on the one hand increases the quality of a description, and on the other hand also results in overfitting. The present study also reveals how ANN methods prefer MCI relatively to MMCI descriptors. Four types of ANN computations show that: (i) MMCI descriptors are preferred with properties with a small number of points, (ii) MLS is preferred over ANN when the number of ANN weights is similar to the number of regression coefficients and, (iii) in some cases, the MLS modeling quality is similar to the modeling quality of ANN computations. Both the common training set and an external randomly chosen validation set were used throughout the paper.

Zeta potential indirectly reflects a charge of the surface of nanoparticles in solutions and could be used to represent the stability of the colloidal solution. As processes of synthesis, testing and evaluation of new nanomaterials are expensive and time-consuming, so it would be helpful to estimate an approximate range of properties for untested nanomaterials using computational modeling. We collected the largest dataset of zeta potential measurements of bare metal oxide nanoparticles in water (87 data points). The dataset was used to develop quantitative structure–property relationship (QSPR) models. Essential features of nanoparticles were represented using a modified simplified molecular input line entry system (SMILES). SMILES strings reflected the size-dependent behavior of zeta potentials, as the considered quasi-SMILES modification included information about both chemical composition and the size of the nanoparticles. Three mathematical models were generated using the Monte Carlo method, and their statistical quality was evaluated (R2 for the training set varied from 0.71 to 0.87; for the validation set, from 0.67 to 0.82; root mean square errors for both training and validation sets ranged from 11.3 to 17.2 mV). The developed models were analyzed and linked to aggregation effects in aqueous solutions.

A quantitative structure property relationship (QSPR) analysis of some organic compounds (imines or Schiff bases) is studied. The corrosion inhibition efficiencies of these imines have been studied by using AM1 (Austin model 1) Hamiltonian SCF-MO method and QSPR analysis. One of the most promising semi-empirical methods for predicating geometries and electronic properties is AM1. The geometry of all compounds 1-11 was optimized and all data obtained for building the multiple regression model. The proposed equations were applied to predict the corrosion inhibition efficiency of some related structures to select molecules of possible activity from a presumable library of compounds was obtained from the regression coefficients for the model.

Lubricants with longer drain interval should be easy to maintain, economical, and environment friendly. Compact and high-performance machines require high-performance lubricants. Under these circumstances, alternative ashless additives exhibiting sufficient high-temperature stability and antioxidancy have to be developed. We have found that the addition of alkylated phenyl salicylates to the lubricants leads to good high-temperature detergency. In this paper, we report the factors affecting the high-temperature detergency of the alkylated phenyl salicylates and their utility.

The stability constant of a complex generally depends on the electronic, stereochemical and steric factors, as well as on the chelate effect and interaction of the complex, metal and ligand with water. In the first part of the paper we discuss the Irving-Williams order of stability of bivalent transition metal ion complexes from Mn2+ to Zn2+, along with the HSAB model (hard and soft acids and bases) of stability. The second part describes three kinds of models for the estimation of the stability constants of complex compounds. First are those based on molecular mechanics, which were used mainly for the estimation of the enantioselectivity effect, i. e. Gibbs energy differences between MLL and MDL isomers. The second kind of models are mechanistic, that rest on the presumption of linear dependence of measured stability constants of the complexes with the same ligand (stability constants of mono- and bis-complexes, protonation constants, etc.). The third kind of models are heuristic (QSPR), which encompass molecular descriptors calculated by the method of overlapping spheres (OS), as well as topological indices. Among the variety of topological indices, connectivity indices proved best. They were calculated for the ligand and various representations of the coordination compound structure.

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.

The Wiener index W is the oldest molecular-graph-based structure-descriptor. It is defined as the sum of the distances of all pairs of vertices of the molecular graph G, where the distance is the number of edges in the shortest path connecting the respective vertices, and where G is the hydrogen-depleted molecular graph. This seemingly very simple topological index could be "upgraded" (a) by using as the distance the sum of the bond lengths along the shortest path, or (b) by using the Euclidean distance between the respective pairs of atoms. Each of these "upgraded" Wiener indices could be computed either (a) for the hydrogen-depleted or (b) for the hydrogen-filled molecular graph. We provide examples showing that none of the modifications (aa), (ab), (ba), (bb) yields better results than the ordinary Wiener index, and that there is a very good linear correlation between W and its "upgraded" variants.

Let G be a molecular graph possessing m0(G) edges. let m1(G) be the number of edges of the line graph L(G) of G, known as the Bertz index. Let m2(G) be the number of edges of the line graph of L(G), etc. We examine the applicability of the sequence mi(G), i = 0, 1, 2, ..., for predicting physicochemical properties of alkanes. Some earlier obtained results are extended and amended.

Let G be a molecular graph possessing m0(G) edges. Let m1(G) be the number of edges of the line graph L(G) of G, known as the Bertz index. Let m2(G) be the number of edges of the line graph of L(G), etc. We examine the applicability of the sequence mi(G), i = 0,1,2, ..., for predicting physicochemical properties of alkanes. Some earlier obtained results are extended and amended.