Structure-property Relationship Models Research Articles

Computation of Vertex-Based Topological Descriptors of Organometallic Monolayers of TM 3 C 12 S 12

Topological descriptors are mathematical values related to chemical structures which are associated with different physicochemical properties. The use of topological descriptors has a great contribution in the field of quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) modeling. These are mathematical relationships between different molecular properties or biological activity and some other physicochemical or structural properties. In this article, we calculate few vertex degree-based topological indices/descriptors of the organometallic monolayer structure. At present, the numerical programming of the biological structure with topological descriptors is increasing in consequence in invigorating science, bioinformatics, and pharmaceutics.

Molecular Descriptor Analysis of Certain Isomeric Natural Polymers

Polymers, drugs, and almost all chemical or biochemical compounds are frequently modeled as diverse ω -cyclic, acyclic, bipartite, and polygonal shapes and regular graphs. Molecular descriptors (topological indices) are the numerical quantities and computed from the molecular graph Γ (2D lattice). These descriptors are highly significant in quantitative structure-property or activity relationship (QSPR and QSAR) modeling that provides the theoretical and the optimal basis to expensive experimental drug design. In this paper, we study three isomeric natural polymers of glucose (polysaccharides), namely, cellulose, glycogen, and amylopectin (starch), having promising pharmaceutical applications, exceptional properties, and fascinating molecular structures. We intend to investigate and compute various closed-form formulas such as ABC , GA , sum-connectivity χ − 1 / 2 , ABC 4 , GA 5 , and Sanskruti indices for the aforementioned macromolecules. Also, we present the closed-form formulas for the first, second, modified, and augmented Zagreb indices, inverse and general Randić indices, and symmetric division deg, harmonic, and inverse sum indices. Furthermore, we provide a comparative analysis using 3D graphs for these families of macromolecules to clarify their nature.

Distance complexity measures versus the orbit-entropy measure of dendrimers

In this article, we compute several distance-based topological indices dendrimers. Indeed, based on the distances of related graphs, we investigate the distance-based complexity of these graphs. We also, determine an orbit entropy measure of these structures based on the size of orbits. The analysis results obtained by computing the correlations between given numerical data shows that the correlation between Winer complexity with GP-index, Wiener index, and Wiener polarity is quite low. As a result, we have inferred that the Wiener-complexity can be considered as new measures for quantitative structure-activity relationship/quantitative structure-property relationship (QSAR/QSPR) models of dendrimers. Hence, the W-complexity might be interesting for further investigations in predicting the physicochemical properties of molecules.

QSPR Models for Predicting Retention Indices of Polygonum minus Huds. Essential Oil Composition Using GA-BWMLR and GA-BPANN Methods

In the present study, quantitative structure-property relationship (QSPR) models were used for modeling and predicting retention index (RI) values of chemical composition in Polygonum minus essential oil. In gas chromatography, RIs are used to convert retention times into system-independent constants. The dataset of 40 molecules was divided into training and external validation sets. A set of molecular descriptors were calculated from the optimized structures of the molecules using Gaussian 09 and Dragon software. The genetic algorithm (GA), backward stepwise multiple linear regression (BW-MLR), and back-propagation artificial neural networks (BPANN) were used to obtain suitable QSPR models. The predictive power of the QSPR model was discussed using a coefficient of determination (R2), average absolute deviation (AAD), mean squared error (MSE), and leave-one-out cross-validation (Q2cv). According to our findings, it was concluded that the QSPR model with three descriptors (ATS5m, BEHm4, and G1) established by the GA-BPANN method could be efficiently used for predicting RI of chemical components in P. minus essential oil and may be helpful for modeling and designing of new molecules of the essential oils.

QSPR MODELLING OF STABILITY CONSTANTS OF METAL-THIOSEMICARBAZONE COMPLEXES USING MULTIVARIATE REGRESSION METHODS AND ARTIFICIAL NEURAL NETWORK

In this study, the stability constants of metal-thiosemicarbazone complexes, logb11 were determined by using the quantitative structure property relationship (QSPR) models. The molecular descriptors, physicochemical and quantum descriptors of complexes were generated from molecular geometric structure and semi-empirical quantum calculation PM7 and PM7/sparkle. The QSPR models were built by using the ordinary least square regression (QSPROLS), partial least square regression (QSPRPLS), primary component regression (QSPRPCR) and artificial neural network (QSPRANN). The best linear model QSPROLS (with k of 9) involves descriptors C5, xp9, electric energy, cosmo volume, N4, SsssN, cosmo area, xp10 and core-core repulsion. The QSPRPLS, QSPR PCR and QSPRANN models were developed basing on 9 varibles of the QSPROLS model. The quality of the QSPR models were validated by the statistical values; The QSPROLS: R2train = 0.944, Q2LOO = 0.903 and MSE = 1.035; The QSPRPLS: R2train = 0.929, R2CV = 0.938 and MSE = 1.115; The QSPRPCR: R2train = 0.934, R2CV = 0.9485 and MSE = 1.147. The neural network model QSPRANN with architecture I(9)-HL(12)-O(1) was presented also with the statistical values: R2train = 0.9723, and R2CV = 0.9731. The QSPR models also were evaluated externally and got good performance results with those from the experimental literature.

QSPR studije karbonilnih, hidroksilnih, polienskih indeksa i prosječne molekulske težine polimera pod fotostabilizacijom pristupom ANN i MLR

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.

Exploring 3D-QSPR models of human skin permeability for a diverse dataset of chemical compounds

The control of permeation is vital not only for the topical application of lotions, creams, and ointments but also for the toxicological and risk assessment of materials from environmental and occupational hazards. To understand the effects of physicochemical properties of a variety of 211 compounds on skin permeability, we developed a three-dimensional quantitative structure-property relationship (3 D-QSPR) model. Alignment free GRid-INdependent Descriptors (GRINDs), which were derived from molecular interaction fields (MIFs) contributed to the regression models. Kennard–Stone algorithm was employed to split data set to a training set of 159 molecules and a test set of 52 molecules. Fractional factorial design (FFD), genetic algorithm (GA) and successive projection algorithm (SPA) were applied to select the most relevant 3 D molecular descriptors. The descriptors selected using various feature selection were correlated with skin permeability constants by partial least squares (PLS) and support vector machine (SVM). SPA-SVM model gave prominent statistical values with the correlation coefficient of = 0.96, Q2= 0.73 and R2pred=0.76. According to the analysis results, the hydrogen bonding donor and acceptor properties of the investigated compounds can influence the penetration into the human skin. Furthermore, it was found that permeability was enhanced by increasing the hydrophobicity and was diminished by increasing the molecular weight. In addition, the presence of hydrophobic groups in the target molecule, as well as their shape and position, can affect the skin permeability.

QSPR based ANFIS model for predicting standard molar chemical exergy of organic materials

Prediction of standard molar chemical exergy value for organic compounds is investigated using a quantitative structure-property relationship (QSPR) model combined with adaptive neuro-fuzzy inference system (ANFIS) strategy. Particle swarm optimization (PSO) method is also implemented to determine the optimal ANFIS structure. The proposed model uses three constitutional descriptors in model development’s procedure. The QSPR-ANFIS model represents a great performance in prediction of the standard molar chemical exergy values with 0.9999, 44548, and 1.49 values for R2, RMSE, and %AARD, respectively.

Prediction of 13C NMR chemical shifts of quinolone derivatives based on DFT calculations

At present, there exists subjectivity in selecting descriptor sets for the quantitative structure property relationship (QSPR) models. A complete set is perfect, in which there is no any element redundant or needed to be added. This paper reports the complete sets of descriptors used to develop QSPR models for 13C NMR chemical shifts (δC parameters) of carbon atoms in quinolone derivatives. These descriptors in the complete sets used are calculated by applying the PBE1PBE functional of density functional theory (DFT) and the 6-311G(2d,2p) basis set. The multiple linear regression (MLR) technique and the support vector machine (SVM) algorithm are, respectively, used to develop linear and nonlinear QSPR models for δC parameters. The four QSPR models have the root mean square (RMS) errors less than 2.0 ppm, which approximately equal one fourth of the errors from the previous model. Further, our models have more samples in the test sets and less descriptors in the models. These results suggest that our four models of δC parameters have better statistical qualities. The feasibility of applying complete sets of descriptors to develop QSPR models for 13C NMR chemical shifts is demonstrated.

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling

ABSTRACTThe present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca+2, Cr+3, Co+2, Cu+2, Cd+2, K+1, Mg+2, Mn+2, Na+1, Ni+2, Pb+2, Zn+2, and Fe+2) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

A modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model for predicting liquid viscosity of pure organic compounds

Liquid viscosity is an important physical property utilized in engineering designs for transportation and processing of fluids. However, the measurement of liquid viscosity is not always easy when the materials have toxicity and instability. In this study, a modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model is suggested and analyzed in terms of its performance of prediction for liquid viscosity compared to the conventional SVRC-QSPR model and the other methods. The modification was conducted by changing the initial point from triple point to ambient temperature (293 K), and assuming that the liquid viscosity at critical temperature is 0 cP. The results reveal that the prediction performance of the modified SVRC-QSPR model is comparable to the other methods as showing 7.90% of mean absolute percentage error (MAPE) and 0.9838 of R 2. In terms of both the number of components and the performance of prediction, the modified SVRC-QSPR model is superior to the conventional SVRC-QSPR model. Also, the applicability of the model is improved since the condition of the end points of the modified model is not so restrictive as the conventional SVRC-QSPR model.

Prediction of solvation enthalpy of gaseous organic compounds in propanol

The purpose of this paper is to present a novel way for developing quantitative structure-property relationship (QSPR) models to predict the gas-to-propanol solvation enthalpy (ΔH solv) of 95 organic compounds. Different kinds of descriptors were calculated for each compound using the Dragon software package. The variable selection technique of replacement method (RM) was employed to select the optimal subset of solute descriptors. Our investigation reveals that the dependence of physical chemistry properties of solution on solvation enthalpy is nonlinear and that the RM method is unable to model the solvation enthalpy accurately. The results established that the calculated ΔH solv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by RM model.

Quantitative Structure-Property Relationship Study to Predict the Retention Times of Some Volatile Compounds in Rosé Wines

In this work, quantitative structure-property relationship (QSPR) models for prediction of retention time (RT) of 47 volatile compounds in rosé wines on the basis of their molecular structures were developed by applying multiple linear regression (MLR) and artificial neural network (ANN). A Levenberg- Marquardt algorithm trained feed-forward back-propagation artificial neural network (ANN) was employed. The data were randomly divided into 29 training and 9 validation sets. For comparison purpose, multiple linear regression (MLR) model of the same data was developed. Cross-validation was used to validate the QSPR models. Also, the application of the models for prediction of external set’s retention times of compounds without any contribution to model development steps was another validation process. The MLR model yielded marginally acceptable statistics with test correlation R2 = 0.993 and mean squared error (MSE) = 0.0075. Not surprisingly, the ANN model was significantly more accurate with test correlation R2 = 0.995 and mean squared error (MSE) = 0.00013.

Prediction of permeability of drug-like compounds across polydimethylsiloxane membranes by machine learning methods

The prediction of maximum steady state flux values of a chemical compound from its structural features plays an important role in design of transdermal drug delivery systems. In this study, we developed the quantitative structure property relationship (QSPR) models to estimate the maximum steady state flux of 245 drugs-like compounds through the polydimethylsiloxane membranes. A correlation-based feature selection was used for descriptor selection. The selected descriptors, surface tension, polarity, and count of hydrogen accept sites, which are interpretable and can be used to explain the permeability of chemicals. These descriptors are used for developing the QSPR prediction models by multiple linear regression, artificial neural network, support vector machine (SVM) and Instance-Based Learning algorithms using K nearest neighbor machine learning approaches. The models were assessed by internal and external validation. All four approaches yield the QSPR models with good statistics. The models developed by SVM have better prediction performance. These models can be useful for predicting the permeability new untested compounds.

Quantitative Structure-Property Relationship Study for Prediction of Flash Point of Some Organic Compounds Based On SW-MLR Method

A quantitative structure-property relationship (QSPR) model was developed for prediction of logFP values of some of organic compounds based on their molecular descriptors. In this study, we have attempted to develop a simple and fast multiple linear regression (MLR) model with high accuracy and precision. The molecular descriptors, which cover different information of molecular structures, were calculated by Dragon software. The most feasible descriptors were selected using stepwise selection. The QSPR model was validated by external set compounds without any contribution in model development step. The accuracy of the suggested model is illustrated using cross-validation, validation through an external test set and Y-randomization. The obtained multivariate linear three descriptors model by MLR has correlation coefficient (R2=0.905, Q2LGO = 0.844, Q2LGO=0.833, REP%=-7.807, F=47.626). All molecular descriptors which have entered in this model are calculated from chemical structure of any molecule. This model was used for the prediction of the flash point values of some organic compounds which were not used in the modeling procedure.

A simple QSPR model for the prediction of the adsorbability of organic compounds onto activated carbon cloth

A quantitative structure-property relationship (QSPR) model was proposed between the molecular descriptors representing the molecular structure and the Freundlich adsorbability parameter (K) for a set of 55 organic compounds onto activated carbon cloth. The best linear model was composed of three descriptors, which were selected by stepwise multiple linear regression (MLR) analysis. The statistical parameters provided by the linear model were r 2 = 0.7744, = 0.7551, s = 0.169 for the training set; and r 2 = 0.6725, = 0.6316, s = 0.196 for the external test set, respectively. The stability and predictive power of the proposed model were further verified using Y-randomization tests, five-fold cross-validation and leave-many-out cross-validation. The model may give some insight into the main structural features that affect the adsorption of the investigated compounds onto activated carbon cloth.

A Quantitative Structure-Property Relationship Study on Photodegradation of Polybrominated Diphenyl Ethers

Quantitative structure-property relationship (QSPR) models were developed in the present work for photodegradation rate constants (kp) of fifteen individual polybrominated diphenyl ethers (PBDEs) in methanol/water (8:2) by UV light in the sunlight region. The molecular descriptors used in the QSPR models were calculated by the two semi-empirical quantum mechanical methods, RM1 and PM6, respectively. Both multiple linear regression (MLR) and artificialneural network (ANN) were applied in this study. The statistic qualities of the MLR models based on the molecular parameters obtained by RM1 and PM6 calculations were both good with the R values of 0.987 and 0.990, respectively. The QSPR model built by the ANN method with the molecular parameters calculated with PM6 is slightly better than that with RM1.

Prediction of monomer reactivity parameters using quantum chemical descriptors

To construct artificial neural network (ANN) models for the prediction of reactivity parameters (u, ν), density functional theory (DFT) calculations are carried out for 55 vinyl monomers, at the B3LYP level of theory with a 6–31G(d) basis set. After the generation of quantum chemical descriptors, the stepwise multiple linear regression (MLR) analysis and the ANN method are used to develop quantitative structure-property relationship (QSPR) models of parameters u and ν. The ANN models produced test set root-mean-square (rms) errors of 0.35 for the parameter u and 0.34 for the parameter ν. Research results indicate that the QSPR models based on DFT calculations and ANN techniques are accurate and possess the ability to generalize.

QSAR and QSPR Model Interpretation Using Partial Least Squares (PLS) Analysis

Carefully developed quantitative structure-activity and structure-property relationship models contain detailed information regarding how differences in the molecular structure of compounds correlate with differences in the observed biological or other physicochemical properties of those compounds. The ability to understand the behavior of existing molecules and to design new molecules is facilitated by using an objective method to extract and explain the details of the underlying structure-activity or structure-property relationship. Furthermore, a clear understanding of how and why compounds behave as they do can lead to new innovations through model-directed selection of compounds to be used in complex mixtures such as laundry detergents, fabric softeners, and shampoos. Such a method has been developed based on partial least-squares (PLS) regression analysis that allows for the identification of specific structural trends that relate to differences in observed properties. But the analysis of the completed model is only the last step of the process. The model development process itself affects the ability to extract a clear interpretation of the model. Everything from the selection of initial pool of molecular descriptors to evaluate to data set and model optimization impacts the ability to derive detailed molecular design information. This review describes the method details and examples of the use of PLS for model interpretation and also outlines suggestions regarding model development and model and data set optimization that enable the interpretation process.

Quantitative structure-property relationship (QSPR) model for predicting acidities of ketones

Ketones are one of the most common functional groups, and ketone-containing compounds are essential in both the nature and the chemical sciences. As such, the acidities (pKa) of ketones provide valuable information for scientists to screen for biological activities, to determine physical properties or to study reaction mechanisms. Direct measurements of pKa of ketones are not readily available due to their extremely weak acidity. Hence, a quantitative structure-property relationship (QSPR) model that can predict the acidities of ketones and their acidity order is highly desirable. The establishment of an acidity scale in dimethyl sulfoxide (DMSO) solution by Bordwell et al. made such an effort possible. By utilizing the pKa values of forty-eight ketones determined in DMSO as the training set, a QSPR model for predicting acidities of ketones was built by stepwise multiple linear regression analysis. The established model showed statistical significance and predictive power (r2 = 0.91, q2 = 0.86, s = 1.42). Moreover, the QSPR model also gave reasonable acidity predictions for five ketones in an external prediction set that were not included in the model generation phase (r2 = 0.92, s = 1.618). Overall, the reported QSPR model for predicting acidities of ketones provides a useful tool for both biologists and chemists in understanding the biophysical properties and reaction rates of different classes of ketones.