Quantitative Structure Property Research Articles

On Computations of Topological Descriptors of Kagome Lattice

The analysis of graphs and networks is a multidisciplinary research field from its origin. As such, it has been extensively used also in biomedicine, cheminformatics, and in bioinformatics, where approaches based on graph descriptors have been offered for communicating with the numerous challenging tasks. In quantitative structure-activity (QSAR) and quantitative structure-property (QSPR) relationships studies, graph invariants are used to forecast the biological activities and properties of nanomaterials. In these studies, degree-based topological descriptors have founded comprehensive acceptance among the different types of descriptors, because of the ease of generation and the speed with which these calculations can be performed. In this article, we first compute the M-polynomials of 2D kagome lattice and then derive the closed form of various significant topological degree-based indices in terms of the M-polynomials and partition techniques. Furthermore, we sketch surfaces that show the dependence of some topological indices on the parameters of the structure.

An HPC hybrid parallel approach to the experimental analysis of Fermat’s theorem extension to arbitrary dimensions on heterogeneous computer systems

In this work, we consider the empirical, computational, study of the generalized Fermat’s last theorem conjecture recently proposed using Minkowski norms defined within natural vector semispaces. This kind of studies relies on the connection of vector spaces to Boolean hypercubes, which relates to systematic natural number generation and, thus, to the inner structure of natural vector sets. This topic is of interest for the vector description of molecular structures among other uses associated with the development of new Quantitative Structure-Properties Relationships, QSPR. Here, we transform the problem into a combinatorial one susceptible of computational analysis. Due to the computational demands of these studies, we resort to a High-Performance Computing (HPC) approach. Thus, arbitrary precision arithmetic is introduced as well as new algorithmic approaches for the computation of Minkowski norms and the application of Fermat’s condition. Besides, the problem is transformed into an embarrassingly parallel one. This permits to develop a hybrid shared-distributed memory parallel approach. To optimize the efficiency of the process on heterogeneous computer systems, a hierarchical dynamic self-scheduling approach is introduced. We find an increase in efficiency of 23% when using the proposed self-scheduling strategy. Application of this HPC approach to about 6000 billion three-dimensional natural vectors, for Boolean hypercubes up to dimension 15, and a range of Minkowski norm powers between 3 and 64, strongly suggests conjecturing that Fermat’s last theorem can be generalized from dimension two to dimension three.

Quantitative Structure-Property Relationship (QSPR) Study on the Complex Compounds Formed From Gadolinium(III) with Dibutyl Dithiophosphate Derivatives Ligands Based on Molecular Descriptors

A study of the quantitative relationship of structure and property (Quantitative Structure Property Relationship (QSPR) has been carried out on complex compounds formed between gadolinium (Gd) and dibutyldithiophosphate (DBDTP) derivative ligands. This study is a part of our laboratory research program on the development of extractant ligands, including DBDTP in extraction for the separation and purification of rare-earth elements (REEs), specifically Gd. Gadolinium has also been a part of the research program about its use in the synthesis of magnetic resonance imaging (MRI) contrast agents, for the diagnosis of various diseases. This chemical calculation research aims to analyze the effect of descriptors in the form of parameters of the physical-chemical properties of bond lengths, bond angles, and bond energies on the stability of Gd complex compounds with DBDTP derivative ligands. To get descriptors PM7 semi-empirical method was used, while for data analysis, Multiple Linear Regression Analysis was used, assuming the model error is normally distributed with zero mean and constant variance. Furthermore, data processing was done using SPSS software. This research was conducted by involving 28 DBDTP derivative ligands and using multiple linear regression analysis. The regression equation is Y ̂ = - 0.966 + 0.586 V1 - 0.014 V2 + 0.000 V3. From the resulted research data it was found that there are three findings, namely: (1) bond length and bond angle have a significant simultaneous effect on stability of Gd complex compounds with DBDTP derivative ligands; (2) bond length and bond angle have a partially significant effect on stability of Gd complex compounds with DBDTP derivative ligands; (3) bond length proved to have a significant dominant effect on stability of Gd complex compounds with DBDTP derivative ligands.

In depth investigation of the retention behavior of structurally related β-blockers on RP-HPLC column: Quality by design and quantitative structure-property relationship complementary approaches for optimization and validation

The persistent introduction of new β-blockers motivates the demand for optimizing RP-HPLC well-designed analytical procedures that could be applied to this structurally related and commonly prescribed pharmacological group in order to reduce time and chemicals consumption in quality control units. Betoxolol HCl (BEX) and Carvidolol (CAR) were selected as representative examples to conduct predictive studies based on two complementary approaches, Quality by design (QBD) and Quantitative structure property relationship (QSPR). In concern QBD, a Box-Behnken design was adopted at variable chromatographic parameters to achieve the most proper conditions that might be applied for efficient analysis of the majority of group members. On the other hand, the retention time was chosen as the target property in the QSPR study that was conducted onto seven β. blockers (the two investigated drugs in addition to five other β. blockers) to find the best correlated molecular descriptors to the retention behavior. Both external and internal validation studies have comparable quality with training levels. Hence a simple selection algorithm of conventional features provides robust confirmatory predictive QBD and QSPR models. Derringer’s desirability function as as a multi-criteria approach was applied for getting the optimum chromatographic analysis conditions. Efficient analysis of BET and CAR was achieved at column temperatures of 26.00 and 27.50 °C, respectively using acetonitrile and phosphate buffer (pH 4.55) 70:30 v/v as a mobile phase with a flow rate of 1.00 mL/min, and UV detection at 220 nm. The method was validated in accordance to ICH guidelines, and had exhibited acceptable precision, accuracy, linearity, and robustness.

Topological aspects of 2D structures of trans-[formula omitted] lattice and a metal-organic superlattice

In quantitative structure property (QSPR) and quantitative structure activity (QSAR) relationships studies, topological indices are utilized to associate the biological activity of underline structures with their physical properties like strain energy, distortion, melting point, boiling point, and stability, etc. In these studies, the degree based topological indices have obtained center position between various kind of descriptors, due to the simplicity of finding and the rapidity with which these estimations can be accomplished. In this paper, we compute the general Randic, first and second Zagreb, Atom Bond Connectivity(ABC) and Geometric Arithmetic(GA) indices of the 2D structures of trans-Pd-(NH2)S lattice and a metal–organic superlattice. Furthermore, we present the precise formulas of the fifth version of Geometric Arithmetic index and the fourth version of Atom-Bond Connectivity index of these lattices.

A Deep Neural Network for Accurate and Robust Prediction of the Glass Transition Temperature of Polyhydroxyalkanoate Homo- and Copolymers.

The purpose of this study was to develop a data-driven machine learning model to predict the performance properties of polyhydroxyalkanoates (PHAs), a group of biosourced polyesters featuring excellent performance, to guide future design and synthesis experiments. A deep neural network (DNN) machine learning model was built for predicting the glass transition temperature, Tg, of PHA homo- and copolymers. Molecular fingerprints were used to capture the structural and atomic information of PHA monomers. The other input variables included the molecular weight, the polydispersity index, and the percentage of each monomer in the homo- and copolymers. The results indicate that the DNN model achieves high accuracy in estimation of the glass transition temperature of PHAs. In addition, the symmetry of the DNN model is ensured by incorporating symmetry data in the training process. The DNN model achieved better performance than the support vector machine (SVD), a nonlinear ML model and least absolute shrinkage and selection operator (LASSO), a sparse linear regression model. The relative importance of factors affecting the DNN model prediction were analyzed. Sensitivity of the DNN model, including strategies to deal with missing data, were also investigated. Compared with commonly used machine learning models incorporating quantitative structure–property (QSPR) relationships, it does not require an explicit descriptor selection step but shows a comparable performance. The machine learning model framework can be readily extended to predict other properties.

Quantitative structure–property relationships for the calculation of the soil adsorption coefficient using machine learning algorithms with calculated chemical properties from open-source software

The soil adsorption coefficient (Koc) is an environmental fate parameter that is essential for environmental risk assessment. However, obtaining Koc requires a significant amount of time and enormous expenditure. Thus, it is necessary to efficiently estimate Koc in the early stages of a chemical's development. In this study, a quantitative structure-property relationship (QSPR) model was developed using calculated physicochemical properties and molecular descriptors with the OPEn structure-activity/property Relationship App (OPERA) and Mordred software using the largest available Koc dataset. Specifically, we compared the accuracies of the model using the light gradient boosted machine (LightGBM), a gradient boosting decision tree (GBDT) algorithm, with those of previous models. The experimental results suggested the potential to develop a QSPR model that will produce highly accurate Koc values using molecular descriptors and physicochemical properties. Unlike previous studies, the use of a combination of LightGBM, OPERA and Mordred enables the prediction of Koc for many chemicals with high accuracy. In this study, OPERA was used to calculate the physicochemical properties, and Mordred was used to calculate molecular descriptors. The wide range of chemicals covered by OPERA and Mordred enables the analysis of a diverse range of chemical compounds. We also report a method to tune the LightBGM program. The use of fast-processing software, such as LightGBM, enables parameter tuning of a method required to obtain best performance. Our research represents one of the few studies in the field of environmental chemistry to use LightGBM. Using physicochemical properties as well as molecular descriptors, we could develop highly accurate Koc prediction models when compared to prior studies. In addition, our QSPR models may be useful for preliminary environmental risk assessment without incurring significant costs during the early chemical developmental stage.

Systematic performance evaluation of gasoline molecules based on quantitative structure-property relationship models

The future production of clean gasoline requires the evaluation of molecular performance. In this paper, we established a systematic performance evaluation model for molecules in the gasoline range. Antiknock, cleanliness, power, stability, and evaporation performance were investigated and are related to the production of high-quality and clean gasoline. Corresponding quantitative structure–property relationship (QSPR) models were developed to predict the octane number, yield sooting index (YSI), combustion heat, unsaturated bond content, and Reid vapor pressure. The obtained models precisely predict these key properties using only the molecular structure as the input. Several applications were made to show the benefit of the molecular performance evaluation model, including the screening of optimal gasoline molecules, the molecular performance distribution of gasoline mixtures, and the effect of the gasoline molecular conversion pathway. The developed model is potentially a useful tool for the identification of optimal gasoline molecules and related process design.

The measure of irregularities of nanosheets

AbstractNanosheets are two-dimensional polymeric materials, which are among the most active areas of investigation of chemistry and physics. Many diverse physicochemical properties of compounds are closely related to their underlying molecular topological descriptors. Thus, topological indices are fascinating beginning points to any statistical approach for attaining quantitative structure–activity (QSAR) and quantitative structure–property (QSPR) relationship studies. Irregularity measures are generally used for quantitative characterization of the topological structure of non-regular graphs. In various applications and problems in material engineering and chemistry, it is valuable to be well-informed of the irregularity of a molecular structure. Furthermore, the estimation of the irregularity of graphs is helpful for not only QSAR/QSPR studies but also different physical and chemical properties, including boiling and melting points, enthalpy of vaporization, entropy, toxicity, and resistance. In this article, we compute the irregularity measures of graphene nanosheet, H-naphtalenic nanosheet, {\text{SiO}}_{2} nanosheet, and the nanosheet covered by {C}_{3} and {C}_{6}.

Quantitative Structure-Properties Relationship of Lubricating Oil Additives and Molecular Dynamic Simulations Studies of Diamond-Like-Carbon (DLC)

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.

Quantitative Structure-Properties Relationships and Molecular Dynamic Simulations of Some Lubricant Additives (LAs)

The degradation of this zinc-dialkyl-dithiophosphate (ZDDP), at a temperature less than 246K often leads to the release of phosphorus, sulphur, and zinc which are indirectly responsible for the emission of poisonous gas from the exhaust pipe of the motor cars. Four QSPR mathematical models were generated from 39 structures of lubricant additives (LAs) and the structural features were found to corresponds to the coefficients of; internal correlation (R2 ) of 0.95, adjusted squared correlation (R2 adj) of 0.94, Cross-validation (Q2cv) of 0.90, and the external validation (R2 pred) of 0.54. The model suggests that new LAs with improved onset temperatures (Tonset) could be designed by interpreting and increasing the value of the molecular descriptor such as IC5 (Information Content index/neighborhood symmetry of 5-order) and Ve (V total size index/weighted by Sanderson electronegativity) and at the same time decreasing the values of RDF080m (Radial Distribution Function-080/weighted by mass), RDF110m (Radial Distribution Function-110/weighted by mass), P2v (2nd component shape directional WHIM index/weighted by Van der Waals volume) and R1e+ (R maximal autocorrelation of lag 1/weighted by Sanderson electronegativity). Moreover, the LAs with an experimental onset temperature of 351.6K agreed with the predicted onset temperature of 351.7K13a. And was also in agreement with the result of molecular dynamics simulations in which the LAs with the best dynamic binding energy of -2112.06 kcal/mol was tightly bounded on the simulated DLC mechanical coated boundary inter-surface and was also found to be better than the commercial LAs, ZDDP in term of binding energy and onset temperature. This investigation will help in rational additive design and synthesis of new and better selective Las

Quantitative structure-property relationship of standard enthalpies of nitrogen oxides based on a MSR and LS-SVR algorithm predictions

Quantum chemistry method is used to calculate 1444 descriptors of each nitrogen oxide molecule. Variables are screened by using multiple stepwise regression (MSR). The optimal ten-element regression equation is derived, its R2 = 0.934 and Q2 = 0.927. The support vector regression (SVR) model between ten molecular descriptors and standard formation enthalpy is established, and the support vector machine is optimized by using the least squares method (LS). The training set consisting of 78 compounds have R2 = 0.969 and Q2 = 0.954. The other 22 compounds constitute the test set to verify the external prediction ability of the model, and its R2 = 0.958. It demonstrates that the LS-SVR model has good stability and prediction ability, and overcomes the problem of over-fitting. The conclusion of quantitative structural property relationship (QSPR) shows that the molecular descriptor and the standard enthalpy of formation are non-linear second-order functions. We speculate that the electronegativity of atoms is the key to determine the standard enthalpy of formation of compounds

Theoretical Investigation of Two Antiabetics Drugs as Corrosion Inhibitors of Aluminium in 1.0 M HCl: Combining DFT and QSPR Calculations

The behavior of two antidiabetic drugs namely N-[(butylamino) carbonyl]-4-methylbenzenesulfonamide or tolbutamide and N-(hexahydrocyclopentapyrrol-2(1H)-ylcarbamoyl)-4-methylbenzenesulfonamide or gliclazide was theoretically evaluated by the Density Functional Theory (DFT) and the Quantitative Structure Property Relationship (QSPR) methods. Theoretical calculations showed a clear correlation between the descriptor parameters and the inhibition efficicencies. It also permitted to identify the reactivity sites. The results reveal that the molecules studied are good inhibitors of aluminium corrosion in 1M hydrochloric acid. Finally, the Quantitative Structure Property Relationship allowed to find the appropriate set of parameters establishing the relationship between the inhibition efficiency and the molecular descriptors.

The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR.

The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.

Thermal Conductivity Estimation of Diverse Liquid Aliphatic Oxygen-Containing Organic Compounds Using the Quantitative Structure-Property Relationship Method.

Thermal conductivity is an essential thermodynamic data in chemical engineering applications. Liquid aliphatic oxygen-containing organic compounds are important organic intermediates and raw materials. As a result, estimating thermal conductivity of liquid aliphatic oxygen-containing organic compounds is of significance in industry production. In this study, the genetic function approximation method was applied to screen descriptors and develop a 6-descriptor linear quantitative structure–property relationship model. The entire data set of these compounds covering 1064 thermal conductivity values was divided into 694-member training set, 298-member test set, and 72-member prediction set. The average absolute relative deviation of the training set, test set, and prediction set were 4.14, 4.41, and 4.16%, respectively. Model validation and Y-randomization test proved that the developed model has goodness-of-fit, predictive power, and robustness. In addition, the applicability domain of the developed model was visualized by the Williams plot. This study can provide a convenient method to estimate the thermal conductivity for researchers in chemical engineering production.

Prediction of corrosion inhibition efficiency of pyridines and quinolines on an iron surface using machine learning-powered quantitative structure-property relationships

Linear and non-linear quantitative structure–property relationship (QSPR) models were developed to predict corrosion inhibition efficiency for a series of 41 pyridine and quinoline N-heterocycles. Out of 20 physicochemical and quantum chemical variables related to the surface adsorption behaviour of the inhibitors, consensus models were constructed using the genetic algorithm-partial least squares (GA-PLS) and genetic algorithm-artificial neural network (GA-ANN) methods. The consensus GA-PLS model comprised of eight variables (exponential of the calculated adsorption energy, LUMO, van der Waals surface area and volume, polarizability, electron affinity, electrophilicity, electron donor capacity) exhibited an %RMSECV of 16.5% and mean %RMSE of 14.9%. Such a model moderately captured the complex relationships between inhibition efficiency and the quantum chemical variables. The consensus GA-ANN model comprised of nine input variables (exponential of the calculated adsorption energy, HOMO, LUMO, HOMO-LUMO Gap, electronegativity, softness, electrophilicity, electron donor capacity and N atomic charge) exhibited an %RMSECV of 16.7 ± 2.3% and mean %RMSE (training/testing/validation) of 8.8%, performing better than its linear counterpart in terms of predictive ability. Both models revealed the importance of adsorption to the metal surface, and electronic parameters quantifying electron acceptance/donation to/from the iron surface, suggesting key corrosion inhibition design principles.

A comparison of molecular representations for lipophilicity quantitative structure-property relationships with results from the SAMPL6 logP Prediction Challenge.

Effective representation of a molecule is required to develop useful quantitative structure-property relationships (QSPR) for accurate prediction of chemical properties. The octanol-water partition coefficient logP, a measure of lipophilicity, is an important property for pharmacological and toxicological endpoints used in the pharmaceutical and regulatory spheres. We compare physicochemical descriptors, structural keys, and circular fingerprints in their ability to effectively represent a chemical space and characterise molecular features to correlate with lipophilicity. Exploratory landscape continuity analyses revealed that whole-molecule physicochemical descriptors could map together compounds that were similar in both molecular features and logP, indicating higher potential for use in logP QSPRs compared to the substructural approach of structural keys and circular fingerprints. Indeed, logP QSPR models parameterised by physicochemical descriptors consistently performed with the lowest error. Our best performing model was a stochastic gradient descent-optimised multilinear regression with 1438 descriptors, returning an internal benchmark RMSE of 1.03 log units. This corroborates the well-established notion that lipophilicity is an additive, whole-molecule property. We externally tested the model by participating in the 2019 SAMPL6 logP Prediction Challenge and blindly predicting for 11 protein kinase inhibitor fragment-like molecules. Our model returned an RMSE of 0.49 log units, placing eighth overall and third in the empirical methods category (submission ID 'hdpuj'). Permutation feature importance analyses revealed that physicochemical descriptors could characterise predictive molecular features highly relevant to the kinase inhibitor fragment-like molecules.

Some Algebraic Polynomials and Topological Indices of Octagonal Network

M-polynomial of different molecular structures helps to calculate many topological indices. A topological index of graph \(G\) is a numerical parameter related to \(G\) which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR), quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Zagreb indices, Randic index, Symmetric division index, Harmonic index, Inverse sum index, Augmented Zagreb index, multiple Zagreb indices etc. are correlated. In this report, we compute closed forms of M-polynomial, first Zagreb polynomial and second Zagreb polynomial of octagonal network. From the M-polynomial we recover some degree-based topological indices for octagonal network.

Quantitative Structure-Property Relationship Analysis Against Critical Micelle Concentration of Sulfonate-Based Surfactant Based on Semiempirical Zindo/1 Calculation

Development of anionic surfactant compound isvery important because the anionic surfactant class iswidely used in people's lives. For instance,anionic surfactantsare used as food additives and detergents. The novelcompound of sulfonate-basedsurfactantor proposed compound has predictedthe CriticalMicelle Concentration(CMC) value of experiment. Quantitative Structure-Property Relationship (QSPR)analysisbased on semiempiricalZINDO/1 calculationwas conducted to obtain QSPR equation. Theoretical predictorsor independent variable which have an influence on the value of CMC are used to construct QSPR equation. The theoretical predictors areclassified intopredictor of electronic properties, solubility and steric. A total of 108experimentalCMCbelongs to sulfonate-basedsurfactant are calculated their theoretical predictors and analyzed by multiple linear regression. The QSPR equationwhich is obtainedfromthis study contains theimportant theoretical predictors.They are solubility properties, molecular weight, molecular size and net charge of carbon atomin thepolar partof sulfonate-based surfactant. This QSPR equation couldbe used to predict the CMC value of the novelsulfonate-based surfactant.

Quantitative Susceptibility Biomarker and Brain Structural Property for Cerebral Cavernous Malformation Related Epilepsy

Quantitative Susceptibility Biomarker and Brain Structural Property for Cerebral Cavernous Malformation Related Epilepsy