Extended Topochemical Atom Models Research Articles

Modeling adsorption of organic compounds on activated carbon using ETA indices

The aim of the present work is to develop quantitative structure–property relationship (QSPR) models for adsorption capability of a large dataset of chemicals (n=3483) on to activated carbon. Two different splitting techniques like k-means clustering and principal component analysis (PCA) combined with duplex method were used to divide the data set into training and test sets. Attempt was made to find out the common descriptors present in various models indicating their importance for adsorption capacity on to activated carbon. In spite of presence of large number of compounds in the training and test sets (3:1 in size ratio), we did not omit any compounds showing outlier behavior to artificially show enhanced values of validation metrics thus ensuring the predictive quality of the models for diverse types of compounds. The models were developed to study the predictive ability of extended topochemical atom (ETA) parameters which are calculated from two-dimensional representation of molecules and introduced by the present group of authors. The ETA models were compared to non-ETA models involving topological, spatial and structural descriptors. In all the cases, the data set was first subjected to stepwise regression to find out the contributing variables, and the selected variables were further subjected to partial least squares (PLS) regression. The PLS models indicate that ETA descriptors provide better external validation characteristics in terms of predictive R2 than that of the non-ETA ones. The best ETA model shows encouraging statistical quality (Qint2=0.8059, Qext(F1)2=0.7914, Qext(F2)2=0.7909, Qext(F3)2=0.8492).

QSPR with extended topochemical atom (ETA) indices: Exploring effects of hydrophobicity, branching and electronic parameters on logCMC values of anionic surfactants

In this study, quantitative structure-property relationship (QSPR) models have been developed to establish the relationship between the molecular structures and the critical micelle concentration (CMC) of 37 anionic surfactants using extended topochemical atom (ETA) indices along with computed hydrophobicity descriptors. The ETA models have also been compared to those developed with non-ETA topological descriptors along with hydrophobicity terms. The selection of the training and test compounds (n=28 and 9, respectively) was done by using a principle component analysis (PCA) score plot. The best ETA model (Q2=0.938; Rpred2=0.923) could outperform in statistical quality and predictive ability the models developed with non-ETA (Q2=0.910; Rpred2=0.885) and combined set (ETA and non-ETA) of descriptors (Q2=0.926; Rpred2=0.915). In this study, it is observed that hydrophobicity plays a major role in the model development for CMC of anionic surfactants while the branching character and electronic nature of the surfactants are also important as evidenced from different ETA parameters.

QSTR with extended topochemical atom (ETA) indices. 15. Development of predictive models for toxicity of organic chemicals against fathead minnow using second-generation ETA indices

Modern industrialisation has led to the production of millions of toxic chemicals having hazardous effects on the ecosystem. It is impracticable to determine the toxic potential of a large number of chemicals in animal models, making the use of quantitative structure–toxicity relationship (QSTR) models an alternative strategy for toxicity prediction. Recently we introduced a set of second-generation extended topochemical atom (ETA) indices for predictive modelling. Here we have developed predictive toxicity models on a large dataset of 459 diverse chemicals against fathead minnow (Pimephales promelas) using the second-generation ETA indices. These descriptors can be easily calculated from two-dimensional molecular representation without the need of time-consuming conformational analysis and alignment, making the developed models easily reproducible. Considering the importance of hydrophobicity for toxicity prediction, AlogP98 was used as an additional predictor in all the models, which were validated rigorously using multiple strategies. The ETA models were comparable in predictability to those involving various non-ETA topological parameters and those previously reported using various descriptors including computationally demanding quantum-chemical ones.

QSTR with extended topochemical atom (ETA) indices. 14. QSAR modeling of toxicity of aromatic aldehydes to Tetrahymena pyriformis

Aldehydes are a toxic class of chemicals causing severe health hazards. In this background, quantitative structure–toxicity relationship (QSTR) models have been developed in the present study using Extended Topochemical Atom (ETA) indices for a large group of 77 aromatic aldehydes for their acute toxicity against the protozoan ciliate Tetrahymena pyriformis. The ETA models have been compared with those developed using various non-ETA topological indices. Attempt was also made to include the n-octanol/water partition coefficient (log K o/w) as an additional descriptor considering the importance of hydrophobicity in toxicity prediction. Thirty different models were developed using different chemometric tools. All the models have been validated using internal validation and external validation techniques. The statistical quality of the ETA models was found to be comparable to that of the non-ETA models. The ETA models have shown the important effects of steric bulk, lipophilicity, presence of electronegative atom containing substituents and functionality of the aldehydic oxygen to the toxicity of the aldehydes. The best ETA model (without using log K o/w) shows encouraging statistical quality ( Q int 2 = 0.709 , Q ext 2 = 0.744 ). It is interesting to note that some of the topological models reported here are better in statistical quality than previously reported models using quantum chemical descriptors.

Exploring QSARs with Extended Topochemical Atom (ETA) Indices for Modeling Chemical and Drug Toxicity

Development of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs) has been practiced for prediction of various toxicities and other relevant properties of chemicals including drug candidates to minimize animal testing, cost and time associated with risk assessment and management processes. This communication reviews published reports of QSARs/QSPRs with Extended Topochemical Atom (ETA) indices for modeling chemical and drug induced toxicities and some physicochemical properties relevant to such toxicities. In each study, ETA models have been compared to those developed using various non-ETA models and it was found that the quality of the QSARs involving ETA parameters were comparable to those involving non-ETA parameters. ETA descriptors were also found to increase statistical quality of the models involving non-ETA parameters when used in combination. On the basis of the reported studies, it can be concluded that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing to the toxicities and these indices may be used in combination with other topological and physicochemical descriptors for development of predictive QSAR models. Such models may be used for virtual screening and in silico prediction of toxicities, and if appropriately used, these may be proved helpful for regulatory decision support and decision making processes.

QSTR with extended topochemical atom (ETA) indices. 13. Modelling of hERG K+ channel blocking activity of diverse functional drugs using different chemometric tools

To accelerate the drug discovery process, early prediction of human ether a-go-go (hERG) K+ channel affinity of drug candidates is becoming an important aspect. We have therefore developed quantitative structure–toxicity relationship models with extended topochemical atom (ETA) indices for hERG K+ channel blocking activity of diverse functional drugs using different chemometric tools like factor analysis followed by multiple linear regression (FA-MLR), stepwise regression and partial least squares. The data set was divided into a training set of 50 compounds and a test set of 17 compounds based on K-means clustering technique. The ETA models were compared with those developed with a pool of other topological indices. Finally, an attempt was made to develop models from the combined pool of topological (ETA and non-ETA) descriptors. It was found that on using ETA parameters along with non-ETA ones, there was a considerable increase in the quality of the models. The best model came from stepwise regression using a combined set of descriptors (Q 2 = 0.546, = 0.619). The ETA model suggests that hERG channel blocking increases with the increase of molecular bulk and electron richness and decreases with the increase of functionalities of the carboxylic acid group and the aliphatic tertiary nitrogen fragment.

QSTR with extended topochemical atom (ETA) indices. 12. QSAR for the toxicity of diverse aromatic compounds to Tetrahymena pyriformis using chemometric tools

We have developed QSTR models for the toxicity of 384 diverse aromatic compounds to Tetrahymena pyriformis with recently introduced extended topochemical atom (ETA) indices and compared the ETA models with those derived from various non-ETA topological descriptors and also combined set of descriptors encompassing the ETA and non-ETA parameters. The data set was split into test (25% compounds of total data points) and training (remaining 75%) sets based on K-mean clustering technique. Different statistical analyses (factor analysis followed by multiple linear regression (FA–MLR), stepwise regression and partial least squares (PLS)) were performed with the training set compounds to develop QSTR models using the topological descriptors. All the developed models were cross-validated using leave-one-out (LOO) technique. The best models were selected on the basis of predicted R 2 values for test set compounds. The best models (based on external validation) developed from different techniques came from the combined set of descriptors. The above results indicate that the use of ETA descriptors with non-ETA descriptors improved the statistical quality of the non-ETA models. From the best models involving ETA parameters, it is observed that functionality of halogen atoms (hydrophobicity), volume parameter (bulk) and nitrogen containing functionalities (polarity) are important for developing QSTR models for the current data set. This study suggests that ETA parameters are sufficient power to encode chemical information contributing significantly to the toxicity of diverse aromatic compounds to T. pyriformis.

QSTR with extended topochemical atom (ETA) indices. 11. Comparative QSAR of acute NSAID cytotoxicity in rat hepatocytes using chemometric tools

An attempt was made to develop quantitative structure–toxicity relationships (QSTRs) for acute rat hepatocyte cytotoxicity of a series of non-steroidal anti-inflammatory drugs (NSAIDs) with extended topochemical atom (ETA) indices. The ETA models were compared with those developed with a pool of other topological indices. Finally, attempt was made to develop models from the combined pool of topological (ETA and non-ETA) descriptors. The chemometric tools used for model development were multiple linear regression with factor analysis as the variable selection tool, stepwise regression, principal component regression analysis, partial least squares (PLS), genetic function approximation (GFA) and genetic PLS (G/PLS). Use of ETA descriptors along with non-ETA ones increased the statistical quality of the non-ETA models in different techniques except stepwise regression and GFA. The best three models came from GFA, G/PLS and PLS techniques (leave-one-out Q 2 values of 0.871, 0.854 and 0.834, respectively, using combined set of descriptors except for GFA). Use of the ETA parameters suggests that the toxicity increases with bulk and degree of branching. Moreover, heteroatom count and degree of unsaturation are also important for the toxicity.

QSTR with extended topochemical atom (ETA) indices. 9. Comparative QSAR for the toxicity of diverse functional organic compounds to Chlorella vulgaris using chemometric tools

Quantitative structure–toxicity relationship (QSTR) studies on toxicity of 91 organic compounds to Chlorella vulgaris have been performed using extended topochemical atom (ETA) indices using different statistical tools like stepwise regression analysis, multiple linear regression with factor analysis (FA-MLR) as the preprocessing step, partial least squares (PLS) regression and principal component regression analysis (PCRA). The ETA models have been compared with the non-ETA ones derived from different topological and physicochemical parameters The results show that the QSTR models using ETA descriptors (FA-MLR: Q 2 = 0.832, PLS: Q 2 = 0.891, stepwise: Q 2 = 0.867, PCRA: Q 2 = 0.741) are comparable to the non-ETA models (FA-MLR: Q 2 = 0.794, PLS: Q 2 = 0.897, stepwise: Q 2 = 0.907, PCRA: Q 2 = 0.772). Improved results are obtained (except in case of PCRA) when we considered ETA and non-ETA descriptors in combination (FA-MLR: Q 2 = 0.850, PLS: Q 2 = 0.913, stepwise: Q 2 = 0.909, PCRA: Q 2 = 0.671). The best two models are obtained using combined ETA and non-ETA descriptors applying stepwise regression [ Q 2 = 0.909] and PLS [ Q 2 = 0.913] techniques. The statistical quality of the best models is better than that of the previously published models. The results suggest that the ETA descriptors are sufficiently rich in chemical information and have ability to encode the structural features contributing significantly to the toxicity of organic chemicals to C. vulgaris.

QSPR of n‐Octanol/Water Partition Coefficient of Nonionic Organic Compounds Using Extended Topochemical Atom (ETA) Indices

AbstractConsidering n‐octanol/water partition coefficient as an important chemical property in modeling the fate and persistence of chemicals, recently introduced Extended Topochemical Atom (ETA) indices have been used to model n‐octanol/water partition coefficient data of 122 nonionic organic compounds which are highly persistent in the environment and reported to bioconcentrate considerably in lipid tissues. In deriving the models, principal component Factor Analysis (FA) followed by Multiple Linear Regression (MLR), stepwise regression, Partial Least Squares (PLS), and Principal Component Regression Analyses (PCRA) were applied as the statistical tools. The model development process was repeated with non‐ETA (topological and physicochemical) descriptors and a combination set comprising both the ETA and non‐ETA descriptors. The models with ETA indices suggested negative contributions of groups capable of hydrogen bonding and/or polar interactions and positive contributions of volume and degree of halogen substitution to the partition coefficient. Finally, we discuss validation of QSPR models by dividing the dataset into training and test sets based on different strategies, e.g., random division, sorted log Kow data and K‐means clusters for the factor scores of the original variable (ETA) matrix without the response property values. The ETA (FA‐MLR and stepwise regression) models were also applied on a purely external dataset (n=35) and acceptable predictive r2 values (0.582 and 0.651 respectively) were obtained. The results suggest that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing to the n‐octanol/water partition coefficient of nonionic organic compounds and thus these merit further assessment to explore their potential in QSAR/QSPR/QSTR modeling.

QSTR with extended topochemical atom (ETA) indices. VI. Acute toxicity of benzene derivatives to tadpoles (Rana japonica)

structure-toxicity relationship (QSTR) studies have proved to be a valuable approach in research on the toxicity of organic chemicals for ranking chemical substances with respect to their potential hazardous effects on living systems. With this background, we have modeled here the acute lethal toxicity of 51 benzene derivatives with recently introduced extended topochemical atom (ETA) indices [Roy and Ghosh, Internet Electron J Mol Des 2:599-620 (2003)]. We also compared the ETA relations with non-ETA models derived from different topological indices (Wiener W, Balaban J, flexibility index, Hosoya Z, Zagreb, molecular connectivity indices, E-state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef,H_bond_donor and H_bond_acceptor). Genetic function approximation (GFA) and factor analysis (FA) were used as the data-preprocessing steps for the development of final multiple linear regression (MLR) equations. Principal-component regression analysis (PCRA) was also used to extract the total information from the ETA/non-ETA/combined matrices. All the models developed were cross-validated using leave-one-out (LOO) and leave-many-out techniques. The summary of the statistics of the best models is as follows: (1) FA-MLR: ETA model- Q 2 (LOO)=0.852, R 2=0.894; non-ETA model- Q 2=0.782, R 2=0.835; ETA + non-ETA model-Q 2 =0.815, R 2=0.859. (2) GFA-MLR: ETA model-Q 2 =0.847, R 2=0.915; non-ETA model-Q 2 =0.863, R 2=0.898; ETA + non-ETA model-Q 2 =0.859, R 2=0.893. 3. PCRA: ETA model-Q 2 =0.864, R 2=0.901; non-ETA model- Q 2=0.866, R 2=0.922; ETA + non-ETA model-Q 2=0.846, R 2=0.890. The statistical quality of the ETA models is comparable to that of non-ETA models. Again, use of non-ETA descriptors in addition to ETA descriptors does not increase the statistical acceptance of the relations significantly. The predictive potential of these models was better than that of the previously reported models using physicochemical parameters [Huang et al., Chemosphere 53:963-970 (2003)]. The relations from ETA descriptors suggest a parabolic dependence of the toxicity on molecular size. Furthermore, the toxicity increases with functionality contribution of chloro substituent and decreases with those of methoxy, hydroxy, carboxy and amino groups. This study suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features that contribute significantly to the acute toxicity of benzene derivatives to Rana japonica.

QSTR with Extended Topochemical Atom Indices. 3. Toxicity of Nitrobenzenes to Tetrahymena pyriformis

AbstractThe experimental determination of toxicological properties of commercial chemicals being costly and time consuming process, there is a need to develop mathematical predictive tool to theoretically quantify such properties. In this background, we have modeled toxicity of nitrobenzene derivatives to Tetrahymena pyriformis using extended topochemical atom (ETA) indices recently introduced by us (Roy and Ghosh, 2003). We have also modeled the toxicity data using other topological descriptors (Balaban J, kappa shape indices, connectivity indices, Wiener index) and two physicochemical variables (AlogP98, MolRef) and compared the ETA models with non‐ETA ones. Principal component factor analysis was used as the data‐preprocessing step to reduce the dimensionality of the data matrix and identify the important variables that are devoid of collinearities. Multiple linear regression analyses show that the best non‐ETA model involves 2κ and AlogP98 as predictor variables and the quality of the relation is as follows: n=42, Q2=0.70, R$ {_{{a}}^{2}}$=0.75, R2=0.76, R=0.87, F=61.0 (df 2, 39), s=0.36. On the other hand, the best ETA model has the following quality: n=42, Q2=0.88, R$ {_{{a}}^{2}}$=0.91, R2=0.92, R=0.96, F=101.4 (df 4, 37), s=0.22. The ETA relations showed positive contributions of molecular bulk (size), halogen and additional nitro substitutions in the nitrobenzene ring and negative contributions of the substituents like methyl and hydroxymethyl groups to the toxicity. An attempt to use non‐ETA descriptors along with the ETA ones slightly improves the quality in comparison to the best ETA model. Interestingly, the ETA model developed by us for the nitrobenzene toxicity is comparable to the previously reported models on the same data set (Estrada et al., 2001; Cronin et al., 1998). Thus, it appears that the ETA descriptors have significant potential in QSAR/QSPR/QSTR studies, which warrants extensive evaluation.