Extended Topochemical Atom Descriptors Research Articles

QSAR modeling with ETA indices for cytotoxicity and enzymatic activity of diverse chemicals

The discharge of huge amount of chemicals from industries into the environment has led to toxicity towards different living species. Therefore, risk assessment of these chemicals is essential. In order to comply with the ethical issues, in this present work, we have developed quantitative structure-activity relationship (QSAR) models for cytotoxicity against GFS (goldfish scale) tissue (Crassius auratus) and enzymatic activity against PLHC-1 cell line (topminnow hepatoma cell line) (Poeciliopsis lucida). The final models were developed by means of PLS (Partial Least Squares) regression method applying only ETA (extended topochemical atom) descriptors. The results obtained from various validation parameters (obtained from the both datasets) suggested that the developed models are statistically robust and predictive. From the insights obtained from the models developed from the Neutral Red dye (NR) dataset, it can be concluded that presence of bulky atoms, unsaturation, branching and hetero atoms (most importantly N, Cl) enhance the cytotoxicity towards the Goldfish scale tissue. On the other hand, in case of the Ethoxyresorufin-O-deethylase (EROD) dataset, presence of higher electronegative atoms (O, Cl), polycyclic aromatic hydrocarbons (PAHs) with more number of rings and absence of polar groups and hydrogen bond acceptors enhance enzymatic activity of the PLHC-1 cell line.

Chemometric modeling of the chromatographic lipophilicity parameter logk0 of ionic liquid cations with ETA and QTMS descriptors

Ionic liquids, though promoted as green solvents, exhibit significant toxicity against various organisms in the ecosystem. The lipophilicity of cations plays a determining role in this toxicity. The experimental lipophilicity values being available for a limited number of cations, we modeled the chromatographically derived lipophilicity parameter logk0 of ionic liquid cations using Extended Topochemical Atom (ETA) descriptors and Quantum Topological Molecular Similarity (QTMS) descriptors. Both types of descriptor were previously found to be important in modeling selected toxicity endpoints of ionic liquids. We have performed both internal and external validation tests and randomization experiments while developing the models. The present study suggests that the ETA and QTMS descriptors are efficient in developing robust and reliable lipophilicity models for cations of ionic liquids. The developed models show that lipophilicity increases with the size of cations, and decreases with their electron-richness and hydrogen bonding propensity. The electronic character of the bond joining the quaternary atom with sp3 hybridized carbon is also important. The computed lipophilicity obtained from the developed model was also applied to the computation of rat toxicity data of a large number of compounds, and gave highly satisfactory results.

Quantitative structure–activity relationship for toxicity of ionic liquids to Daphnia magna: Aromaticity vs. lipophilicity

Water solubility of ionic liquids (ILs) allows their dispersion into aquatic systems and raises concerns on their pollutant potential. Again, lipophilicity can contribute to the toxicity of ILs due to increased ability of the compounds to cross lipoidal bio-membranes. In the present work, we have performed statistical model development for toxicity of a set of ionic liquids to Daphnia magna, a widely accepted model organism for toxicity testing, using computed lipophilicity, atom-type fragment, quantum topological molecular similarity (QTMS) and extended topochemical atom (ETA) descriptors. The models have been developed and validated in accordance with the Organization for Economic Co-operation and Development (OECD) guidelines for quantitative structure–activity relationships (QSARs). The best partial least squares (PLS) model outperforms the previously reported multiple linear regression (MLR) model in statistical quality and predictive ability (R2=0.955, Q2=0.917, Rpred2=0.848). In this work, the ETA descriptors show importance of branching and aromaticity while the QTMS descriptor ellipticity efficiently shows which compounds are influential in the data set, with reference to the model. While obvious importance of lipophilicity is evident from the models, the best model clearly shows the importance of aromaticity suggesting that more lipophilic ILs with less toxicity may be designed by avoiding aromaticity, nitrogen atoms and increasing branching in the cationic structure. The developed quantitative models are in consonance with the recent hypothesis of importance of aromaticity for toxicity of ILs.

Modeling bioconcentration factor (BCF) using mechanistically interpretable descriptors computed from open source tool “PaDEL-Descriptor”

Predictive regression-based models for bioconcentration factor (BCF) have been developed using mechanistically interpretable descriptors computed from open source tool PaDEL-Descriptor ( http://padel.nus.edu.sg/software/padeldescriptor/ ). A data set of 522 diverse chemicals has been used for this modeling study, and extended topochemical atom (ETA) indices developed by the present authors' group were chosen as the descriptors. Due to the importance of lipohilicity in modeling BCF, XLogP (computed partition coefficient) was also tried as an additional descriptor. Genetic function approximation followed by multiple linear regression algorithm was applied to select descriptors, and subsequent partial least squares analyses were performed to establish mathematical equations for BCF prediction. The model generated from only ETA indices shows importance of seven descriptors in model development, while the model generated from ETA descriptors along with XlogP shows importance of four descriptors in model development. In general, BCF depends on lipophilicity, presence of heteroatoms, presence of halogens, fused ring system, hydrogen bonding groups, etc. The developed models show excellent statistical qualities and predictive ability. The developed models were used also for prediction of an external data set available from the literature, and good quality of predictions (R (2) pred = 0.812 and 0.826) was demonstrated. Thus, BCF can be predicted using ETA and XlogP descriptors calculated from open source PaDEL-Descriptor software in the context of aquatic chemical toxicity management.

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, 3: Modeling of critical micelle concentration of cationic surfactants

The ability of surfactant molecules to facilitate solubilization of essential chemical entities accounts for their wide application in the pharmaceutical industry. The solubilization ability begins at concentrations exceeding the critical micelle concentration (CMC) which in turn is influenced by the molecular structure of the surfactants. Here, attempts have been made to explore the essential molecular fragments determining CMC of cationic surfactants using in silico technique. Different classes of descriptors (ETA and non-ETA) were correlated with the CMC data of a series of cationic surfactants to develop predictive models. Models were developed using genetic function approximation technique and were extensively validated employing different validation statistics. Comparative analysis was performed between the best model developed using the ETA descriptors and that obtained with the non-ETA variables. The results revealed that the external predictive potential of the ETA model was superior to the non-ETA model. The results also indicated that the ETA model could efficiently determine the CMC profile of the untested molecules. Finally, the two classes of descriptors were combined and the QSPR model thus obtained exhibited improved external predictive potential. The ETA descriptors implicated the importance of molecular size and their degree of unsaturation while the non-ETA descriptors signified impact of the bond order and the number of fragments bearing hydrogen atoms attached to carbon atoms with variable hybridization. Thus, addition of ETA descriptors improved the model quality extensively identifying essential molecular fragments contributing to their CMC profile and can be comprehensively utilized for property prediction of new cationic surfactants.

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. 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 the bioconcentration factors of non-ionic organic compounds in fish using extended topochemical atom (ETA) indices

Bioconcentration refers to the absorption or uptake of a chemical from the media to an organism's tissues leading to greater concentration in tissues than that in the surrounding environment. Considering the importance of bioconcentration from the viewpoint of ecological safety assessment, a QSPR study was conducted based upon log BCF of 122 non-ionic organic compounds in fish using the recently introduced extended topochemical atom (ETA) indices. 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 analysis (PCRA) were applied as statistical tools. This was repeated with non-ETA (topological and physicochemical) descriptors and a combination set including both the ETA and non-ETA descriptors. The ETA indices suggested negative contributions of functionalities of nitro, amino and hydroxy substructures and positive contributions of branching, volume and functionality of chloro substituents. Again, the predictive ability of the developed models was compared with the previously reported models. Finally the validation of all the QSAR models was discussed based on random division, sorted log BCF data and K-means clusters for the factor scores of the original variable (ETA) matrix without the response property values. The results suggest that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing to the bioconcentration of the non-ionic organic compounds in fish and thus these merit further assessment to explore their potential in QSAR/QSPR/QSTR modelling.

QSTR with Extended Topochemical Atom (ETA) Indices 8.a QSAR for the inhibition of substituted phenols on germination rate of Cucumis sativus using chemometric tools

AbstractQuantitative structure‐toxicity relationship (QSTR) study has proved to be a valuable approach in ecotoxicity estimations of acute and chronic toxicity to various organisms, and in fate estimations of physical/chemical properties, degradation, and bioconcentration. In this background, we have modeled the inhibition of 41 substituted phenols on germination rate of Cucumis sativus with extended topochemical atom (ETA) indices using different chemometric tools and compared the ETA relations with non‐ETA models derived from different topological indices (Wiener W, Balaban J, Hosoya Z, Zagreb, molecular connectivity indices, E‐state indices and kappa shape indices) and physicochemical parameters (AlogP98, MolRef, H_bond_acceptor, H_bond_donor, MW, logKow and Elumo). Different statistical tools used in this communication are stepwise regression analysis, multiple linear regression with factor analysis as the preprocessing step for variable selection (FA‐MLR), multiple linear regression with genetic function approximation (GFA‐MLR), partial least squares regression with factor analysis as the preprocessing step for variable selection (FA‐PLS), genetic partial least squares (G/PLS) and principal component regression analysis (PCRA). The results show that the best statistical quality along with prediction statistics with ETA descriptors is found in case of GFA‐MLR equation. In case of non‐ETA descriptors, the best equation is obtained from PCRA considering both equation statistics and predictive ability. When combined descriptors were considered, FA‐PLS gave the best equation. The best ETA equation (GFA‐MLR) has better leave‐one‐out Q2 value than the best non‐ETA equation (PCRA). The results suggest that the ETA descriptors are sufficiently rich in chemical information to encode the structural features contributing significantly to the comparative inhibition activity of substituted phenols on germination rate of Cucumis sativus.

QSTR with Extended Topochemical Atom Indices. 7. QSAR of Substituted Benzenes to Saccharomyces cerevisiae

AbstractThe experimental determination of toxicological properties of commercial chemicals being costly and time consuming process, there is the need to develop mathematical predictive tools to theoretically quantify such properties. In this background, we have modeled the nonspecific toxicity of 51 substituted benzenes to the yeast Saccharomyces cerevisiae using extended topochemical atom (ETA) indices. Principal component factor analysis (FA) 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 (MLR) analyses show that the best ETA model has the following statistical quality: n= 51, Q2= 0.851, Ra2= 0.874, R= 0.940, F= 87.9 (df 4, 46), s= 0.235, PRESS= 3.3. We have also modeled the toxicity data using other topological descriptors including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E‐state parameters apart from physicochemical parameters like AlogP98, MolRef, H_bond_acceptor and H_bond _donor. The best model shows the following quality: n= 51, Q2=0.837, Ra2 = 0.855, R= 0.929 , F= 98.9 (df 3,47), s= 0.253, PRESS=3.6. An attempt to use a combined set including both ETA and non‐ETA parameters comes out with the following results: n=51, Q2=0.824, Ra2=0.852, R= 0.940, F= 73.0 (df 4,46), s= 0.255, PRESS=3.9. Besides FA‐MLR, stepwise regression analysis and partial least squares (PLS) analysis were used as additional statistical tools. The use of the ETA indices suggested negative contributions of functionalities of amino and carboxylic acid substituents on the benzene ring and the presence of the electronegative atoms and positive contributions of branching and functionality of chloro substituent. Using factor scores as independent variables, principal component regression analysis (PCRA) was performed and the derived relations were of the following statistical qualities: Q2 values being 0.926, 0.878 and 0.869 while R2 values being 0.942, 0.903 and 0.899 for factor scores derived from ETA, non‐ETA and combined matrices respectively. Thus, it appears that the ETA descriptors have significant potential in QSAR/QSPR/QSTR, which warrants extensive evaluation.

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. Part 5: Modeling of the acute toxicity of phenylsulfonyl carboxylates to Vibrio fischeri using genetic function approximation

In continuation of our recent efforts to model the acute toxicity of 56 phenylsulfonyl carboxylates to Vibrio fischeri using principal component factor analysis, the present paper deals with modeling of the same data set with extended topochemical atom (ETA) indices using genetic function approximation (GFA) as the statistical tool. The statistical quality of the best model using ETA descriptors is as follows: n = 56, Q 2 = 0.771, R a 2 = 0.861 , R = 0.935, F = 69.0 (df5,50), s = 0.172, AVRES = 0.128. This equation was better in statistical quality than that obtained previously using principal component factor analysis as the data-preprocessing step. An attempt was also made to model the data set with different nonETA parameters (topological indices including Wiener, Hosoya Z, molecular connectivity, kappa shape, Balaban J and E-State parameters apart from physicochemical parameters like Alog P98, MolRef and H_bond_acceptor) and the best model showed the following quality: n = 56, Q 2 = 0.805, R a 2 = 0.820 , R = 0.913, F = 53.5 (df4,51), s = 0.196, AVRES = 0.136. An attempt to use both ETA and nonETA parameters lead to an equation which is marginally better than the best ETA model: n = 56, Q 2 = 0.779, R a 2 = 0.865 , R = 0.937, F = 71.7 (df5,50), s = 0.169, AVRES = 0.127. Use of the ETA indices suggested negative contributions of steric bulk, functionality of C 10, volume of substituents on C 10 and functionalities of chloro and nitro substituents at X 1 position and positive contributions of functionalities of C 4 and C 1 and presence of substituents with electronegative atoms, especially at R 2 and R 3 positions. Furthermore, absence of substituents at R 2 and R 3 positions decreases toxicity. This study corroborates the results obtained from principal component factor analysis and suggests that ETA parameters are sufficiently rich in chemical information to encode the structural features contributing significantly to the acute toxicity of phenylsulfonyl carboxylates to V. fischeri.

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.

QSTR with extended topochemical atom indices. 2. Fish toxicity of substituted benzenes.

Considering the importance of quantitative structure-toxicity relationship (QSTR) studies in the field of aquatic toxicology from the viewpoint of ecological safety assessment, fish toxicity of various benzene derivatives has been modeled by the multiple regression technique using recently introduced extended topochemical atom (ETA) indices. The toxicity data have also been modeled using other selected topological descriptors and physicochemical variables, and the best ETA model has been compared to the 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. All-possible-subsets regression was also applied on the parameters to cross-check the variable selection for the best model. Multiple linear regression analyses show that the best non-ETA model involves 1chi, ALogP98, and LUMO (energy) as predictor variables and the quality of the relation is as follows: n = 92, Q2 = 0.718, Ra2 = 0.730, R2 = 0.738, R = 0.859, F = 82.8 (df 3, 88), s = 0.340. On the other hand, the best ETA model has the following quality: n = 92, Q2 = 0.865, Ra2 = 0.876, R2 = 0.885, R = 0.941, F = 92.6 (df 7, 84), s = 0.230. The ETA relations showed positive contributions of molecular bulk (size), chloro and hydroxy substitutions in the benzene ring, and the simultaneous presence of methyl and nitro substitutions to the toxicity. Further, the presence of fluoro and ether functionality, amino or nitro functionality in an otherwise unsubstituted ring, and nitro functionality that is ortho to a chloro substituent decreases toxicity. An attempt to use non-ETA descriptors along with ETA ones did not improve the quality in comparison to the best ETA model. Interestingly, the ETA model developed presently for the fish toxicity is better than the previously reported models on the same data set. Thus, it appears that ETA descriptors have significant potential in QSAR/QSPR/QSTR studies, which warrants extensive evaluation.