Electrotopological State Indices Research Articles

Machine learning-based QSAR modeling, molecular docking, dynamics simulation studies for cytotoxicity prediction in MDA-MB231 triple-negative breast cancer cell line

Triple-negative breast cancer (TNBC) is the most aggressive cancer type that tests negative for progesterone, estrogen, and HER2 protein receptors. Therefore, TNBC is unlikely to respond either to drugs that target HER2 receptors or to hormonal therapy drugs. In the present study, 2D-QSAR model was developed to predict the inhibitory concentration (IC50) of terpene derivatives against human breast cancer metastatic cell line MDA-MB231, using V-Life MDS v4.5, module. The model was developed using the forward stepwise multiple linear regression method, with a regression coefficient (r2) of 0.86, and a cross-validated r2 (q2) of 0.8448. Molecular descriptors namely electronegativity (Epsilon-3), carbon atoms separated through five bond distances (T_C_C_5), Sum of Electrotopological state indices of –CH2 group (SssCH2count), Dipole moment of coordinate-z (Zcomp Dipole), and Distance between highest positive and negative electrostatic potential on van-der Waals surface area (Most +ve & -ve Potential Distance) were found to be the most potent contributing chemical descriptors for cytotoxicity against TNBC MDA-MB231 cell line. Additionally, binding affinities and interaction patterns revealed that the proposed compounds exhibit good binding affinities and substantial stability with c-Met-and β-tubulin receptors, as assessed by docking studies. Molecular dynamics simulations (100 ns) and binding free energy calculations were also performed using the MMGBSA method. The pharmacokinetic and eADME/T analysis of predicted compounds were assessed through Discovery Studio software. These findings may be of immense importance in the optimization and development of dual inhibitory potent anti-cancer inhibitors against the MDA-MB231 TNBC cell line. Moreover, this novel QSAR based prediction model/method is implemented in R-package and software tool developed for virtual screening purposes and available online for download through the public repository GitHub.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Prediction of thermal decomposition temperatures of binary imidazolium ionic liquid mixtures using improved E-state index descriptors

Ionic liquid (IL) mixtures have been widely used in various fields as new green “design solvents”. However, ILs are often used at high temperatures, which may trigger thermal hazards. The thermal decomposition temperature (Td) is an important parameter to characterize their thermal hazards. In this work, a quantitative structure-property relationship (QSPR) method is used to develop a model for predicting Td of binary imidazolium IL mixtures. Twelve kinds of mixing rules are used to improve the original electrotopological state (E-state) index descriptors, which can better describe the interaction of binary IL mixtures as well as the structural characteristics. By using the random forest (RF) method to build prediction models, two models with three descriptors (R2 = 0.974) and four descriptors (R2 = 0.977) are obtained by comparing their predictive capability. The various validations have demonstrated that those two models have good robustness and predictive capabilities. This work provides two reliable models to predict the Td of binary imidazolium IL mixtures, which is expected to provide theoretical guidance for the safe use of binary imidazolium IL mixtures.

Modeling of Aquatic Toxicity of a Set of Phenols in Silico

A structure / lethal dose 50 (pCIC50) relationship was researched for a set of phenols while favoring a hybrid genetic algorithm (GA) / multiple linear regression (MLR) approaches to the structural parameters being computed with (E-calc) which calcula the Kier–Hall Electrotopological state indices (E- state) and Hyperchem software. Among the more than 100 simple models with two explanatory variables acquired, we chose the model with the best values of the prediction parameter (Q2) and the coefficient of determination (R2). The reliability of the proposed model has also been illustrated using various techniques of evaluation: leave-many out, cross-validation, randomization test, and validation by the test set. pCIC50 = - 0.0835 ± (0.07006) +0.112 ± (0.007408 (logkow)2 - 0.116 ± (0.01797) s-CH3 ntot = 81 ; S= 0.3296 log unit ; Q2(%) = 74.26 ; R2 (%)= 79.24 ; F= 118.3193; P=0,000.

Machine learning the frontier orbital energies of SubPc based triads.

Organic photovoltaic devices are promising candidates for efficient energy harvesting from sunlight. Designing new dye molecules suitable for such devices is a challenging task restricted by the rapid increase of computational cost with system size. Solar cell material properties are closely related to the electronic structure of the dye, and an effective molecular orbital energy screening method for a family of dyes is therefore desired. In this work, a machine learning approach is used to sort through the chemical space of peripheral double-substituted boron-Subphthalocyanine dyes. A database of 12,102 PM6 optimized structures was built and for each of the structures time-dependent density functional theory (LC-[Formula: see text]HPBE/6-31+G(d)) calculations were performed. We investigated the changes of the molecular orbital energies of the molecular orbitals related to reduction and oxidation of the compounds. With the Electrotopological-state index moleculear representation all the tested algorithms, Support Vector Machine, Random Forest Regression, Neural Network, and Simple Linear Regression, captured the calculated frontier orbital energies with a prediction root-mean-square-error in the order of 0.05 eV. Finally, frontier orbital energies were predicted for more than 40,000 new structures by the trained Support Vector Machine algorithm. Compared to the parent boron-Subphthalocyanine structure, 237 and 132 functionalized dyes were predicted to have upshifted molecular orbital energies using the Electrotopological-state index and OneHot encoding feature vector, respectively. Out of 27 investigated donor and acceptor ligands, the acetamide and hydroxyl ligands gave rise to the desired increase in frontier molecular orbital energy.

New mathematical measures for apprehending complexity of chiral molecules using information entropy.

In this paper, we present several new theoretical measures based on information entropy that can be used to analyze the information content of a chiral molecule. Starting from a differentiation between "chiral" and "achiral" portions in a chiral molecule, we define a new concept that allows us to quantify the complexity of chiral constitutional 2D-isomers of C10 to C20 alkanes. Various new chiral and achiral information measures founded on joint entropy, mutual information, and conditional entropy are presented providing an access to a set of regression equations. Then, introducing a case-based measure of entropy, we demonstrate that the distribution of the chiral complexity in these molecules is mostly skewed-right: 60% of the chiral isomers follow a 60/40 distribution rule, which indicates a concentration of chiral complexity in a small number of topological features. Furthermore, by replacing 2D topological distances by 3D distances, the application of these new information measures goes from conformational to racemization and deracemization studies. Interestingly, when the geometrical distances between atoms and the chiral center(s) are taken into account when determining the chiral information entropy, one can observe a significative Pearson correlation coefficient (R= 0.70) between the chiral entropy of 3D molecules and the continuous chirality measure. Finally, we show that our approach is applicable to almost any type of chiral organic chemical structures if in the entropy equation, atoms are represented by their electrotopological state (E-state) index instead of connectivity.

In silico de novo Design of NNRTIs of HIV-1: Functional Group Based Computational Molecular Modeling Approach

Seven novel lead compounds, acting as NNRTIs of HIV-1, are extracted from a database of, in silico de novo designed, 500 compounds. Functional group based computational molecular modelling techniques are used for such design of Acylthiocarbamate derivatives. Effect of structural characteristics on the antiviral activity of these derivatives has also been studied. Statistical regression techniques namely, Non-linear (Back Propagation Neural Network, Support Vector Machine) and linear (Multiple Linear) chemometric regression methods are used in developing the relationships of Kier-Hall Electrotopological State Indices (E RingA, E O8, E N9, E O14, E S16, E N17, E O19, E R, and E R1 ) with the HIV-1 antiviral activity. The results of assessment of relative potentials of these methods suggest that BPNN (r 2 = 0.845, MSE = 0.142, q 2 = 0.818) describes the relationship between the descriptors and antiviral activity in a better manner than SVM - ε-radial (r 2 = 0.844, MSE = 0.144, q 2 = 0.807) and MLR (r 2 = 0.836, MSE = 0.150, q 2 = 0.805).

QSPR Studies on the Octane Number of Toluene Primary Reference Fuel Based on the Electrotopological State Index

The quantitative structure–property relationship (QSPR) models for predicting the octane number (ON) of toluene primary reference fuel (TPRF; blends of n-heptane, isooctane, and toluene) was investigated. The electrotopological state (E-state) index of TPRF components was computed and weight-summed to generate the quantitative descriptor of TPRF samples. The partial least squares (PLS) technique was used to build up the regression model between the ON and weight-summed E-state index of the investigated samples. The QSPR models for the research octane number (RON) and motor octane number (MON) of TPRF were built. The prediction performance of the obtained PLS models was assessed by the external test set validation and leave-one-out cross-validation. The validation results demonstrate that the proposed PLS models are feasible for predicting the ON, both RON and MON, of TPRF. In addition, several other QSPR models for the ON of TPRF were developed by employing the stepwise regression and Scheffé polynomials methods, and the prediction performance of these models were compared with that of the PLS models. The comparison result shows that the proposed PLS models are slightly better than multiple linear regression models and Scheffé models. It is demonstrated that the combination of the E-state index and PLS is an easy-to-use and promising method for studying and forecasting the ON of TPRF.

Predicting glass transition temperature of polyphenol molecular based on its structure

Abstract Glass transition is an extremely important phenomenon in condensed physics. It greatly affects the physical property of materials. In this paper, we divide molecular structures into some different types of atom groups according to electrotopological state indices theory. Taking molecular structures and relative molecular mass as variables, we construct models through linear regression and support vector regression (SVR) respectively. Meanwhile we studied how to divide molecular structures into different bonds, considering that the protective degree can directly affect the number of several bonds. Therefore, first we do principle component analysis to eliminate the correlation of the variables by using SVR to construct models. And then, we make comparisons of the two different methods. It is concluded that using the types of atom group to predict the glass transition temperature can achieve higher accuracy.

Comprehensive QSAR studies reveal structural insights into the NR2B subtype selective benzazepine derivatives as N-Methyl-d-Aspartate receptor antagonists

NMDA receptors are considered as high profile therapeutic target in the treatment of pain and neurodegenerative diseases such as Alzheimer's, Huntington's and Parkinson's disease. NR2 subunit of NMDA receptor divided into four subunits i.e. NR2A, NR2B, NR2C and NR2D. Restricted distribution of NR2B subunit in the brain makes it potential target. In the present study, new structural insights into benzazepine derivatives as NR2B selective NMDA receptor antagonists have been reported using QSAR modelling. Total five QSAR models were developed using various statistical methods. 2D-QSAR models showed that chi3 and SsssNE-index descriptors are crucial for NMDA receptor antagonistic activity. Chi3 descriptor relates with non-hydrogen heteroatom connected with two or three bonds and SsssNE-index descriptor deals with Electrotopological state indices for number of nitrogen atom connected with three single bonds. Both the descriptors were negatively correlated with all the developed 2D-QSAR models. Significance of these descriptors is extensively studied in present work. In 3D-QSAR studies, steric and electrostatic fields were found to be important for antagonistic effect on NMDA receptors. 3D-QSAR studies clarify the substitution pattern of R group on nitrogen atom of benzazepine core. The substitutions with high electropositive with less or medium steric character are favourable for the activity. All developed models were critically validated to establish their reliability and accuracy for prediction of binding affinity of benzazepine derivatives towards NR2B subunit of NMDA receptor. The present study offered not only highly predictive and reliable QSAR models for benzazepine derivatives but also revealed some of the vital structural observations required for their higher binding affinity towards NR2B subunit of NMDA receptors.

QSAR Study on the Relative Activity of Thiazide diuretics in Doping

Electrotopological state indices (Ek) of atom types is used to describe the structures of 10 thiazide diuretics in doping and related to the relative activity(Ra). The optimal three-parameter(E16, E23, E9) quantitative structure-activity relationships (QSAR) model is established by using leaps-and-bounds regression analysis for the relative activity(Ra) of thiazide diuretics along with the Ek. The correlation coefficients (R2) and the leave-one-out (LOO) cross validation RCv2 for the Ra was 0.975 and 0.936, respectively. The model has favorable correlation, as well as robustness and good prediction capability by F, Rcv2, Aic, Fit, Vif tests. The results indicate that the molecular structural units: = O(in sulfuryl), = N- and = C<(in aromatic ring) are main factors which can affect the relative activity(Ra) bioactivities of these compounds directly.

Machine-Learning Energy Gaps of Porphyrins with Molecular Graph Representations.

Molecular functionalization of porphyrins opens countless new opportunities in tailoring their physicochemical properties for light-harvesting applications. However, the immense materials space spanned by a vast number of substituent ligands and chelating metal ions prohibits high-throughput screening of combinatorial libraries. In this work, machine-learning algorithms equipped with the domain knowledge of chemical graph theory were employed for predicting the energy gaps of >12 000 porphyrins from the Computational Materials Repository. Among a variety of graph-based molecular descriptors, the electrotopological-state index, which encodes electronic and topological structure information, captures the energy gaps of porphyrins with a prediction RMSE < 0.06 eV. Importantly, feature sensitivity analysis suggests that the carbon structural motif in methine bridges connected to the anchor group predominantly influences the energy gaps of porphyrins, consistent with the spatial distribution of their frontier molecular orbitals from quantum-chemical calculations.

Prediction of Dispersive Liquid–Liquid Microextraction Enrichment Effect of Aromatic Organics by [OMIM] [PF6] Ionic Liquid Based on Atom-Type Electrotopological State Indices

In this work, a quantitative structure–property relationship (QSPR) model based on atom-type electrotopological state (E-state) indices has been developed for predicting the dispersive liquid–liquid microextraction (DLLME) enrichment effect for aromatic organics by using 1-octyl-3-methylimidazolium hexafluorophosphate ([OMIM][PF6]) ionic liquid as extractant. The enrichment factors of the aromatic organics obtained through DLLME-IL process were used as the dependent variable of the multiple linear regression (MLR) equation. The E-state indices of those organic compounds which combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors and the independent variable of the equation. The squared correlation coefficient R2 for the MLR was 0.992 for the training set of 33 compounds. For the test set of 8 compounds, the linear regression coefficient was R2 = 0.985. The results of this study showed that it can successfully predict enrichment factors of [OMIM][PF6] in DLLME by using atom-type E-state indices, which can provide one more way for predicting the ability of ionic liquid to extract organic compounds based on their molecular structures.

QSPR study on the flash point of organic binary mixtures by using electrotopological state index

The quantitative structure property relationship (QSPR) for the flash point of 288 organic binary mixtures was investigated. The electrotopological state (E-state) index of the components in each mixture was calculated and weight summed to generate the quantitative descriptor of the investigated mixtures. Multivariable linear regression (MLR), stepwise regression and radial basis function artificial neural network (RBF-ANN) was respectively used to build the calibration model. The weight summed E-state index was used as the independent variable of the established models. The prediction performance of the developed models were assessed with external test validation, k-fold cross validation and Monte Carlo cross validation (MCCV). The results of the three validations demonstrate that the RBF-ANN model which includes five input variables is the best one among the developed models. The prediction root mean square relative error (RMSRE) of the external test validation, k-fold cross validation and MCCV is 1.86, 1.11 and 1.07 respectively for this model. It is demonstrated that there is a quantitative relationship between the E-state index and flash point of the investigated mixtures. MLR, stepwise regression and RBF-ANN are all practicable for modeling this relationship. The developed RBF-ANN model involving five input variables is the most promising method for predicting the flash point of organic binary mixtures.

Application of Group-Based QSAR and Molecular Docking in the Design of Insulin-Like Growth Factor Antagonists

Purpose: To identify the structural requirements for designing a lead key for insulin-like growth factor (IGF-1R) inhibition using group-based quantitative structure activity relationship (GQSAR) and molecular docking.Methods: GQSAR method requires fragmentation of molecules. The molecules in the current dataset were fragmented into three (R1, R2 and R3) by applying common fragmentation pattern, and fragmentbased 2D descriptors were then calculated. GQSAR models were derived by applying various methods including multiple linear regressions and partial least square or k-nearest neighbour.Results: Four generated GQSAR models were selected based on the statistical significance of the model. It was found that the presence of flexible and non-aromatic groups on fragment R1 was conducive for inhibition. Additionally, the existence of amino groups as hydrogen bond donors at fragments R2 and R3 was fruitful for inhibition. Docking studies revealed the binding orientation adopted by the active compounds at several amino acid residues, including Met 1126, Arg, 1128, Met 1052, GLU 1050, Met 1112, Leu 1051, Met 1049, Val 1033, and Val 983 at ATP binding sites of IGF-1R kinase domain.Conclusion: The generated models provide a site-specific insight into the structural requirements for IGF-1R inhibition which can be used to design and develop potent inhibitors.Keywords: Insulin-like growth factor 1 (IGF-1) receptor, Quantitative structure-activity relationship, Adenosine triphosphate, Competitive inhibitors, Electrotopological state index.

Research on Neural Network Theory of the Toxicity of Aromatic Hydrocarbons on &lt;i&gt;Chlorella vulgaris&lt;/i&gt;

Using BP neural network method, we calculate and analyze the molecular structure of aromatic hydrocarbons. Then, we get the electrotopological state indices and the molecular electronegativity distance vectors of 25 aromatic hydrocarbons based on the calculation of molecular structure characteristics and adjacency matrix. By regression, we get and optimize the structural parametersE9,E13,E17andM15. The four structural parameters are used as the input variables and a 4-2-1 network structure is employed to construct a BP artificial neural network model for predicting acute toxicitypEC50. The total correlation coefficientRis 0.994 and the average error between the predicted value and experimental value ofpEC50is 0.079, which indicate that the ANN model has good stability and superior predictive ability. The results show that there is a good nonlinear correlation between acute toxicitypEC50and the four structural parameters. The results of our research reveal that the toxicity of aromatic hydrocarbons is closely affected by electrotopological state indices and the molecular electronegativity distance vectors. Therefore, it will be helpful in assessing the hazard of aromatic hydrocarbons to environment.

The Anti-Influenza Activity of Cyclohexene Derivatives Studied by Topological Indices and Neural Network

In order to establish quantitative structure-activity relationship (QSAR) model of cyclohexene derivatives as neuraminidase inhibitors, the relationships between the anti-influenza activity of 34 cyclohexene derivatives and their molecular connectivity indices (mX) as well as their electrotopological state indices (Im) were analyzed, meanwhile molecular structures of the substances were also effectively characterized. On the one hand, two molecular connectivity 0X, 3X and four electrotopological state indices I1, I3, I6, I16, which were obtained by optimization of multiple linear stepwise regression, were employed to found QSAR model through stepwise regression analysis, where the correlation coefficient of equation was 0.906 and the average absolute error between experimental values and estimates was 0.37. On the other hand, Using these structural parameters above as the input neurons, a satisfying neural network model with the 6-2-1 network architecture was also constructed by the back-propagation (BP) algorithm, where the total correlation coefficient reached up to 0.966 and the average absolute error between experimental values and estimates of the model decreased to 0.19. Therefore it was concluded that the results of BP network were better than those of multiple linear regression methods.

Environmental toxicological fate prediction of diverse organic chemicals based on steady-state compartmental chemical mass ratio using quantitative structure–fate relationship (QSFR) models

Four quantitative prediction models for steady-state compartmental chemical mass concentrations (Wn,g) were obtained from structural information, physiochemical properties, degradation rate and transport coefficients of 455 diverse organic chemicals using chemometric tools in a quantitative structure–fate relationship (QSFR) study. The mass ratio assessment of environmentally prevalent organic chemicals may be helpful to predict their toxicological fate in the ecosystems. Four sets of mass ratios [(1) log(Wair) from water emissions (water to air compartment), (2) log(Wair) from air emissions (within different zones of the air compartment), (3) log(Wwater) from water emissions (within different zones of the water compartment) and (4) log(Wwater) from air emissions (air to water compartment)] have been used. The developed models using genetic function approximation followed by multiple linear regression (GFA-MLR) and subsequent partial least squares (PLS) treatment identify only four descriptors for log(Wair) from water emission, six descriptors for log(Wair) from air emission, five descriptors for log(Wwater) from water emission and seven descriptors for log(Wwater) from air emission for predicting efficiently a large number of test set chemicals (ntest=182). The conclusive models suggest that descriptors such as partition coefficients (Kaw, Kow and Ksw), degradation parameters (Ksoil,Kwater and Kair), vapor pressure (Pv), diffusivity (Dwater), spatial descriptors (Jurs-WNSA-1, Jurs-WNSA-2, Jurs-WPSA-3, Jurs-FNSA-3 and Density), thermodynamic descriptors (MolRef and AlogP98), electrotopological state indices (S_dsN, S_ssNH and S_dsCH) are important for predicting the chemical mass ratios. The developed models may be applicable in toxicological fate prediction of diverse chemicals in the ecosystems.

Synthesis, antitubercular activity, and QSAR analysis of substituted nitroaryl analogs: chalcone, pyrazole, isoxazole, and pyrimidines

In the present investigation, 4-nitroacetophenone, on condensation with appropriate aldehydes in ethanolic sodium hydroxide solution, yielded the corresponding chalcones. These corresponding chalcones were reacted with hydrazine hydrate, urea, thiourea, and hydroxylamine hydrochloride, which led to the formation of corresponding novel pyrazole, pyrimidine, and isooxazole derivatives, respectively. All newly synthesized compounds were evaluated for their antimycobacterial activities against Mycobacterium tuberculosis using agar dilution. The results indicated that most of the compounds demonstrated better in vitro antitubercular activity, and the compound 1g has a MIC of 1.56 μg mL−1. QSAR analysis revealed that molecular descriptors mean electrotopological state (Ms) and average valance connectivity index chi-0 (0χAv) contributed positively while 3-path Kier alpha-modified shape index (3κα) contributed negatively. The objective of our study is to generate new leads and to optimize their structure to display the potent efficacy.

Development of Structure Information from Molecular Topology for Modeling Chemical and Biological Properties: A Tribute to the Creativity of Lemont Burwell Kier on his 80th Birthday

This review is a salute to Monty Kier's creativity. Emphasis is placed on creative aspects in the development of the representation of molecular topological structure information and the resultant formalisms: molecular connectivity and electrotopological state (E-State). Less attention is given to detailed analysis of individual papers and the generally well known books and book chapters. This discussion reveals creative paths that led to the concept of the atomic descriptors, simple connectivity delta, encoding local topology, and valence delta value which encodes valence electron information. The fundamental developments that led to the creation of molecular connectivity chi indices are described along with extensions to different chi and delta chi formalisms. Continued thinking about structure in the topological sense led to the development of the only valence state electronegativity formalism based entirely on structure, Kier-Hall electronegativity (KHE). That creation further inspired the development of the electronegativity/topology-based atomic intrinsic state along with perturbation terms that together give electrotopological state indices (E-State). Further creation led to atom and bond type E-State descriptors. All these developments are briefly illustrated with examples in QSAR, chemical similarity, and database searching.