Electrotopological Descriptors Research Articles

A novel application of Kier–Hall electrotopological descriptors in datamining new HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) on the basis of chemical similarity

A novel application of Kier–Hall electrotopological descriptors in datamining new HIV-1 non-nucleoside reverse transcriptase inhibitors (NNRTIs) on the basis of chemical similarity

Silico Methodologies Modelling of Aquatic Toxicity in Tetrahymena Pyriformis Via Aromatic Amines

EU Directive for the Protection of Laboratory Animals mandates and encourages the use of alternative methods that could substitute, cut down on, and generally improve animal testing. Quantitative structure-activity relationship models (QSAR) as well as in vitro toxicity testing are among the most notable of such. QSARs are defined as computerized mathematical models that can utilize a compound’s (aromatic amine) biological activity—aquatic toxicity—to calculate or provide the experimental descriptors of the chemical structure of this compound. Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) are the approaches we use for the aim of predicting aquatic toxicity. The best models for two descriptors are the electrotopological descriptors derived from E-calc, and the partition coefficient derived by the Hyperchem software, applying a genetic algorithm—variable subset selection procedure. The important values of the statistical parameters obtained by the two approaches were as follows: By MLR: R2= 92.18, Q2 = 90.51, Q2ext= 95.26, F=188.5466, S = 0.1995. By ANN were: Q2 = 94.79, RMSE= 0.16, Q2ext= 91.71, RMSEext=0.18.

Explainable machine learning for unraveling solvent effects in polyimide organic solvent nanofiltration membranes

Understanding the effects of solvents on organic solvent nanofiltration currently depends on results obtained from small datasets, which slows down the industrial implementation of this technology. We present an in-depth study to identify and unify the effects of solvent parameters on solute rejection. For this purpose, we measured the rejection of 407 solutes in 11 common and green solvents using a polyimide membrane in a medium-throughput cross-flow nanofiltration system. Based on the large dataset, we experimentally verify that permeance and electronic effects of the solvent structure (Hildebrand parameters, electrotopological descriptors, and LogP) have strong impact on the average solute rejection. We furthermore identify the most important solvent parameters affecting solute rejection. Our dataset was used to build and test a graph neural network to predict the rejection of solutes. The results were rigorously tested against both internal and literature data, and demonstrated good generalization and robustness. Our model showed 0.124 (86.4% R2) and 0.123 (71.4 R2) root mean squared error for the internal and literature test sets, respectively. Explainable artificial intelligence helps understand and visualize the underlying effects of atoms and functional groups altering the rejection.

Predicting formation of haloacetic acids by chlorination of organic compounds using machine-learning-assisted quantitative structure-activity relationships

The presence of disinfection byproducts (DBPs) in drinking water is a major public health concern, and an effective strategy to limit the formation of these DBPs is to prevent their precursors. In silico prediction from chemical structure would allow rapid identification of precursors and could be used as a prescreening tool to prioritize testing. We present models using machine learning algorithms (i.e., support vector regressor, random forest regressor, and multilayer perceptron regressor) and chemical descriptors as features to predict the formation of haloacetic acids (HAAs). A robust model with good predictivity (i.e., leave-one-out cross-validated Q2 > 0.5) to predict the formation of trichloroacetic acid (TCAA) was developed using a random forest regressor. The number of aromatic bonds, hydrophilicity, and electrotopological descriptors related to electrostatic interactions and the atomic distribution of electronegativity were identified as important predictors of TCAA formation potentials (FPs). However, the prediction of dichloroacetic acid was less accurate, which is congruent with the presence of different types of precursors exhibiting distinct mechanisms. This study demonstrates that nonlinear combinations of general chemical descriptors can adequately estimate HAAFPs, and we hope that our study can be used to predict precursors of other disinfection byproducts based on chemical structures using a similar workflow.

Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions

In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).

Computational study of diarylcyclopentene derivatives as selective prostaglandin EP1 receptor antagonist: QSAR approach

2D quantitative structure–activity relationships (2D QSAR) studies were performed on a series of diarylcyclopentene derivatives as prostaglandin EP1 receptor antagonist. To establish the relationship between Prostaglandin EP1 receptor and diarylcyclopentene derivatives, a 2D-QSAR model based on individual, estate numbers, structural, electro topological and baumann alignment descriptors parameters was developed. The best model-1 correlation coefficient with r2 = 0.8101, significant cross validated correlation coefficient (q2 = 0.7491) and for external test set pred_r2 = 0.7812 developed by Partial Least Squares method. The QSAR model reveals that hydroxyl and methoxy group at R position on the diarylcyclopentene moiety is responsible for improving the prostaglandin EP1 receptor activity. The results of 2D QSAR studies were used to design new molecules and to predict their prostaglandin EP1 receptor activity using the developed models.

An approach to design antimicrobial agents by 2D QSAR studies on series of 5, 5-diphenylimidazolidine-2, 4-dione derivatives

A series of twenty two molecules substituted 5,5-diphenylimidazolidine-2,4-dione derivatives displaying variable inhibition of microbial activity were selected to develop models for establishing 2D QSAR by multiple linear regression analysis. The compounds in the selected series were characterized by spatial, molecular and electro topological descriptors using QSAR model of molecular design suite (V-Life MDS 3.5). Correlations between inhibitory activities and calculated predictor variables were established through multiple linear regression method. The whole dataset was divided into training set (17 compounds) and test set (05 compounds). The statistically significant best 2D QSAR model having correlation coefficient r 2 = 0.8686 and cross validated squared correlation coefficient q 2 = 0.7994 with external predictive ability of pred_r 2 = 0.6791 coefficient of correlation of predicted data set (pred_r 2 se) 0.0999 was developed by stepwise MLR method with the descriptors like SSCH 3 , SdSE-index, SsssN Count and Xlogp. These results should serve as a guideline in designing more potent and selective antimicrobial and antifungal molecules.

Prediction of antibacterial and antifungal activity of 4 –benzylideneamino –benzene sulfonamides series by 2D QSAR studies

A series of thirty one molecules substituted 4-benzylideneamino benzenesulphonamides derivatives compounds displaying variable inhibition of microbial activity were selected to develop models for estab- lishing 2D QSAR by partial least square analysis. The compounds in the selected series were char- acterized by spatial, molecular and electrotopological descriptors using QSAR model of molecular design suite (V-Life MDS 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The whole dataset was divided into training set (22 compounds) and test set (09 compounds). The statistically signi cant best 2D QSAR model having correlation coefcient r2 = 0.8482and cross validated squared cor- relation coef cient q2 = 0.8094with external predictive ability of pred_r2 = 0.0947 coef cient of correlation of predicted data set (pred_r2se) 0.0879 was developed by stepwise PLSR method with the descriptors like T_2_N_5, T_2_N_3, SsssNE-index, SssOH Eindex Count, SsBrcount and Xlogp. These results should serve as a guideline in designing more potent and selective amtimicrobial molecules.

Predictive QSAR modeling of substituted Phenylpyrazinones as corticotropin-releasing factor-1 (CRF1) receptor antagonists: computational approach

A QSAR study has been performed on a series of Phenylpyrazinones derivatives with potent corticotropin-releasing factor-1 (CRF1) receptor antagonists. Structural features responsible for the activity of the compounds were characterized by using physicochemical, topological, and electrotopological descriptors, calculated from the Molecular Design Suite software. The statistically significant 2D-QSAR model having r 2 = 0.8141 and q 2 = 0.7391 with pred_r 2 = 0.7827 was developed by partial least squares method. Results reveal that the 2D-QSAR studies signify positive contribution of SsOHcount and SsCH3 count toward the biological activity, whereas negative contribution of 1PathCoun will be in favor of higher CRF1 activity. The QSAR model indicated that the T_2_F_1, T_C_Cl_1 and SaasCE-index played an important role in determining CRF1 receptor antagonists. Their corticotropin-releasing factor 1 capacity can be increased by number and position of the chlorine group. These correlations will be helpful in the development of Phenylpyrazinones as CRF1 receptor activities with a much more enhanced potency.

Molecular modelling studies for the discovery of new substituted pyridines derivatives with angiotensin II AT1 receptor antagonists.

A QSAR study has been performed on a series of pyridines derivatives with potent angiotensin II AT1 receptor antagonists. Structural features responsible for the activity of the compounds were characterized by using topological, electrotopological, group based and 3D descriptors, calculated from the Molecular Design Suite software (V-life MDS 3.5). To elucidate the structural properties required for antihypertensive activity, four different molecular modeling techniques; two-dimensional, Group-based (G-QSAR), k-nearest neighbour and pharmacophore approach. A suitable set of molecular descriptors was calculated and stepwise - partial component regression (SW-PCR) was employed to select the descriptors that resulted in the models with the best fit to the data. This study was performed with twenty two compounds using sphere exclusion algorithm method for the division of the data set into training and test set. The statistically significant 2D QSAR model having r(2) = 0.8407 and q(2) = 0.7395 with pred_r(2) = 0.7971 was developed by stepwise-partial component regression (SW-PCR) and best Group based QSAR model having R(2) = 0.8132 and Q(2) = 0.6804 with pred_r(2) = 0.7661 was developed by SW-PCR. The analyzed k-nearest neighbour MFA model revealed a good fit, having q(2) value of 0.7635. The predictive power of the model generated was validated using a test set molecules with pred _r(2) value of 0.7314. The generated k-nearest neighbour models suggest that steric and electrostatic interactions play an important role in describing the variation in binding affinity. Additionally the pharmacophore model well corraborated with k-nearest neighbour studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The present analysis has shown that the antihypertensive activity can be improved with the presence of specific steric substituent and electro-donating and electro-withdrawing groups nearby the pyridine moiety. The Pharmacophore information shows that the four features used were two AroC feature, one HAc, one AlaC features. The structural variations in the molecular fields at particular regions in the space provide underlying structural requirements and 3D-QSAR models generated give good predictive ability and aid in the design of potent antihypertensive activity.

Pharmacophore and QSAR modeling of some structurally diverse azaaurones derivatives as anti-malarial activity

Quantitative structure–activity relationship (QSAR) and Pharmacophore studies were performed on a series of 35 azaaurones derivatives to find out the structural requirements of their antimalarial activities. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDSTM 3.5). Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression method. The developed QSAR models were found to be statistically significant with respect to training (r 2 > 0.6), and cross-validation (q 2 > 0.6). Simulated annealing (SA) and stepwise (SW) regression are applied as variable selection methods for an effective comparison and model development. This study was performed with 35 compounds (data set) using the sphere exclusion (SE) algorithm method for the division of the data set into training and test sets. The statistically significant 2D-QSAR model having r 2 = 0.9061 and q 2 = 0.8150 with pred_r 2 = 0.8719 was developed by SW-PLS confirms a positive contribution of T_O_Cl_6, T_C_Cl_1, SsOHcount, T_C_F_2 and SsCH3E index to the anti-malarial activity and best Group-based QSAR (GQSAR) model having r 2 = 0.7624 and q 2 = 0.7341 with pred_r 2 = 0.7461 was developed by SA-PLS. The result two-dimensional QSAR and GQSAR clearly explained that substitution of topological indices, hydrophobic properties and auto-correlation descriptors of different atomic properties at R1 and R2 on aurone ring is essential for the activity. Further analysis using three-dimensional QSAR technique identifies a suitable model obtained by SA-partial least square method leading to anti-malarial activity prediction. Molecular field analysis was used to construct the best 3D-QSAR model using k-nearest neighbor method, showing good correlative and predictive capabilities in terms of q 2 = 0.6906 and pred_r 2 = 0.7454. Additionally the pharmacophore model well corroborated with k-nearest neighbor studies as the contours of later were in good agreement with the 3D orientation of the pharmacophoric features. The distances between the pharmacophore sites were measured in order to confirm their significance to the activities. The results reveal that the acceptor, donor, and aromatic/hydrophobic pharmacophore properties are favorable contours sites for the activities. The results of 2D-QSAR, GQSAR and k-nearest neighbor-based 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these substituted azaaurones derivatives as anti-malarial activity, which can provide guidance for the rational design of novel potent azaaurones derivatives.

QSAR studies for the computational prediction of HMG-CoA reductase inhibitors by genetic function approximation technique

Two-dimensional quantitative structure−activity relationship (2D-QSAR) models are useful in understanding how chemical structure is related to the biological activity of natural and synthetic chemicals. Also, they could be usefully employed for designing newer and better therapeutics. A 2D-QSAR study was performed for 52 compounds of a series of thiophenyl quinolines and α-asarone derivatives as potential hypocholesterolemic inhibitors using different types of physicochemical descriptors, which correlated significantly with the activity. Linear QSAR models were developed using multiple linear regression, where the genetic algorithm (genetic function approximation technique) was adopted for selecting the most appropriate descriptors. The results are discussed on the basis of regression data and the cross-validation technique. Model A is the best 2D-QSAR model describing the inhibition efficiency of HMG-CoA reductase with cross-validated squared correlation coefficient (Q 2 = 0.700) and the squared correlation coefficient (R 2 = 0.752), which is able to describe 70% of the variance in the experimental activity. The good agreement between the experimental and the predicted values of pIC50 (micromoles per litre) (R = 0.876) confirms the reliability and the predictability of the proposed model. The results obtained from the present QSAR study explained the importance of the electronic, structural, spatial, and electrotopological descriptors in enhancing the biological activity of the investigated inhibitors.

Molecular modeling and pharmacophore approach for structural requirements of some 2-substituted-1-naphthols derivatives as potent 5-lipoxygenase inhibitors

Molecular modeling and pharmacophore study has been performed on a series of 2-Substituted-1-naphthols derivatives with potent 5-lipoxygenase inhibitory activity. Structural features responsible for the activity of the compounds were characterized by using physicochemical, topological, and electrotopological descriptors, calculated from the Molecular Design Suite Software (V-life MDS™ 3.5). The relationship developed between biological activity and electrostatic, steric, hydrophobicity parameters was explored to generate combinatorial library with various substitution. This library can be explored for the synthesis and study of future potential anti-inflammatory agent. The requirements for the 5-lipoxygenase inhibitory activity are explored with 2D-QSAR, group based QSAR (G-QSAR), and k-nearest neighbour studies. The statistically significant 2D-QSAR model having r 2 = 0.8502 and q 2 = 0.8144 with pred_r 2 = 0.8072 and the best group based QSAR (G-QSAR) model having r 2 = 0.9137 and q 2 = 0.7808 with pred_r 2 = 0.8490 was developed by simulated annealing based partial least squares method. Further analysis using k-nearest neighbour 3D-QSAR technique identifies two models obtained by simulated annealing (SA) and genetic algorithm (GA)-based partial least squares methods leading to 5-lipoxygenase activity prediction. The best simulated annealing QSAR model showed q 2 = 0.8547, r 2 = 0.8963, and standard error = 0.3677. It was observed that steric properties predicted by k-nearest neighbour MFA contours can be related to 5-lipoxygenase activity. The predictive ability of the resultant model was evaluated using a test set molecules and the predicted r 2 = 0.8162. The best pharmacophore model with hydrophobic, hydrogen bond donor (HBD), and aromatic features has root mean square deviation (RMSD) of 0.5746. The QSAR model suggests that electron-withdrawing character is favorable for the 5-lipoxygenase inhibitory activity. This suggests that electron-withdrawing groups like a chlorine or fluorine atoms at naphthol moiety are good for anti-inflammatory activity. In addition to the electron-withdrawing character, electron-donating groups, hydrophobic and hydrogen bond donor groups positively contribute to the 5-lipoxygenase inhibition. The results of 2D-QSAR, Group based QSAR, and k-nearest neighbour 3D-QSAR studies give detailed structural insights as well as highlights important binding features of these 2-Substituted-1-naphthols derivatives as anti-inflammatory agents which can provide guidance for the rational design of novel potent 5-lipoxygenase inhibitors.

A simple electrotopological index for quantitative structure-activity relationship correlation of physical properties with biomolecular activities

Quantitative structure-activity relationship (QSAR) studies constitute a process by which the physicochemical properties of a set of chemical structures are quantitatively correlated with a measurable, such as the concentration of a substance required to give a certain therapeutic drug response. 2D-QSAR studies start with 10–20 analogues, ranging from biologically active to inactive; each analogue, regardless of bioactivity, is described by a series of descriptors. To further broaden the practical utility of these simple descriptors we have sought to identify hybrid indices which are trivial to calculate but which capture data from at least two categories of descriptors. An electrotopological descriptor, termed ET\(_\mathrm{Z}\), which combines electronic information and molecular topology, has been devised and validated against a set of 25 anticonvulsant hydantoin molecules. This ET\(_\mathrm{Z}\) is based solely on atomic connectivity information obtained from the graph without explicit input from molecular geometry.

QSTR mathematical models for the toxicity of aliphatic carboxylic acids on tetrahymena pyriformis

The present work represents an attempt to improve QSTR models for aquatic toxicity of 3838 aliphatic carboxylic acids tested in the Tetrahymena pyriformis population growth assay, using the topological index ZEP and the following nine main electrotopological and molecular descriptors: acidic dissociation constant, 1-octanol/water partition coefficient, the energy of the lowest unoccupied molecular orbital, acidic dissociation constant, molar refractivity, refraction index, surface tension, polarizability and, electrotopological states. Several different relations between toxicity [loglog(IGC50-1)] and the molecular and topological properties were examined, and a group of multiple linear regression models with high fitness scores were generated.

Editorial (Hot Topic: Topological and Electrotopological Descriptors of Molecules: Fundamental Principles and Applications to Computer Aided Molecular Design – Part II)

Editorial (Hot Topic: Topological and Electrotopological Descriptors of Molecules: Fundamental Principles and Applications to Computer Aided Molecular Design – Part II)

Editorial [Hot Topic: Topological and Electrotopological Descriptors of Molecules: Fundamental Principles and Applications to Computer Aided Molecular Design – Part I (Guest Editors: Subhash C. Basak and Guillermo Restrepo)

Editorial [Hot Topic: Topological and Electrotopological Descriptors of Molecules: Fundamental Principles and Applications to Computer Aided Molecular Design – Part I (Guest Editors: Subhash C. Basak and Guillermo Restrepo)

2D-QSAR Studies of Substituted Pyrazolone Derivatives as Anti-Inflammatory Agents

A QSAR study was performed on a series of substituted pyrazolone derivatives. The compounds in the selected series were characterized by spatial, molecular and electrotopological descriptors using QSAR module of molecular design suite (V-Life MDS 4.0). In present research paper we reported 2D-QSAR studies of substituted pyrazolone derivatives. Correlations between inhibitory activities and calculated predictor variables were established through partial least square regression (stepwise forward) method.f

Modeling VEGFR kinase inhibition of aminopyrazolopyridine urea derivatives using topological and physicochemical descriptors: a quantitative structure activity analysis study

A QSAR study has been performed on a series of aminopyrazolopyridine urea derivatives with potent VEGFR kinase inhibitory activity. Structural features responsible for the activity of the compounds were characterized by using physicochemical, topological, and electrotopological descriptors, calculated from the Molecular Design Suite software (V-life MDSTM 3.5). Correlations between the inhibitory activities (KDR and KDRcell) of aminopyrazolopyridine urea derivatives and the calculated descriptors were established through simulated annealing method. The best QSAR models generated from the study explain 89 and 88% of the variation in KDR and KDRcell inhibitory activities, respectively. Internal and external validation methods were used to evaluate the predictive capacity of the generated models. The significant cross-validated correlation coefficient (Q 2 > 0.6) and other statistical parameters suggest that the models exhibited considerable predictivity. The generated QSAR models divulge that factors related to lipophilicity and topological state of atoms in the molecule influences KDR and KDRcell inhibitory activities of aminopyrazolopyridine urea derivatives. A QSAR study has been performed on aminopyrazolopyridine urea derivatives which exhibited VEGFR kinase inhibitory activity, and the results derived show that the lipophilicity and the topological state of atoms in the molecule influences KDR and KDRcell inhibitory activities.

Structural analysis of α-glucosidase inhibitors by validated QSAR models using topological and hydrophobicity based descriptors

In the present investigation, QSAR analysis was performed on a data set consist of structurally diverse compounds in order to investigate the role of its structural features on their α-glucosidase inhibitory activity. The computational and statistical analyses were performed with V-life MDS and Statistica software. The models derived from multiple linear regression analysis were validated by LOO, LMO, Y-randomization and external test validation methods to investigate its reliability and robustness. The MLR models for the complete data set were also developed with the same descriptors to examine the reliability of the contributed descriptors (in the training set models) for the activity prediction. The validated QSAR models show that the molecular connectivity descriptors (ChiV3cluster and ChiV4), electrotopological descriptors (SdOE-Index and SaasCE-Index) and the hydrophobicity descriptor are contributed significantly for the stability of the models. The molecular connectivity descriptors reveal that the molecules with less number of branches on carbon or hetero atoms along with the presence of electronegative atoms are important for the inhibitory activity. The hydrophobicity descriptor, XKMosthydrophilichydrophobic distance (XKMHD) (calculated by Kellog method using XlogP) demonstrates that the XlogP values between the hydrophobic and hydrophilic distance should be low for favorable activity. The electrotopological descriptors such as the number of oxygen atoms connected with one double bond (SdOE-Index) and the number of carbon atom connected with one single bond along with two aromatic bonds (SaasCE-Index) are favorable for the activity. This study concluded that the presence of balanced hydrophobic and hydrophilic (polar and/or electron negative) properties on the vdW surface area is responsible for hydrogen bonding and other electrostatic interaction (aromatic) of inhibitors with the enzyme for favorable α-glucosidase inhibitory activity.