Atom-type Electrotopological State Indices Research Articles

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 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.

A new method for predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds

A new method is presented for estimating the gross and net heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. Elemental compositions as well as the presence of some specific polar groups and molecular fragments are important parameters in the new model. The novel method can be easily used for any complex organic compounds with at least one nitro, nitramine or nitrate functional groups by which the predictions of their heats of combustion by the available methods are inaccurate or difficult. The predicted results show that this method gives reliable predictions of heats of combustion with respect to group additivity method and computed values based on atom-type electrotopological state indices for several energetic compounds where the models can be applied.

A comparative study concerning chromatographic retention and computed partition coefficients of some precursors of peraza crown ethers

Abstract Retention indices for some precursors of peraza crown ethers were determined by reversed phase high-performance thin layer chromatography on RP-18 plates with methanol-water in different volume proportions as mobile phase. The Log P values for the same compounds were calculated using different computer programs: SciQSAR, SciLogP, Chem3D Ultra 8.0, XLOGP (based on atom contributions), Chemaxon and KOWWIN (based on atom/fragment contributions), cLogP (based on fragmental contributions), ALOGPS and IAlogP (based on atom-type electrotopological-state indices and neural network modeling). A comparative study concerning lipophilic parameters (RM0, b and ϕ0) and computed partition coefficients has been developed. Taking into account the correlation coefficients between determined and calculated Log P values, it seems that RM0 and b are less suitable than ϕ0 for estimating lipophilicity of the compounds investigated, and cLogP and ALOGPS provide the best correlations with experimental values.

Prediction of the net heat of combustion of organic compounds based on atom-type electrotopological state indices

An accurate quantitative structure–property relationship (QSPR) model, based on the atom-type electrotopological state (E-state) indices and artificial neural network (ANN) technique, for prediction of standard net heat of combustion (Δ H c o) was developed. An extended set of 49 atom-type electrotopological state (E-state) indices that combined together both electronic and topological characteristics of the analyzed molecules were used as molecular structure descriptors for a diverse set of 1496 organic compounds. Both multilinear regression (MLR) and artificial neural network (ANN) were employed in the modeling. The ANN model with the final optimum network architecture of [49-35-1] gave a significant better performance than the MLR model. The squared correlation coefficient R 2 for the ANN model was R 2 = 0.991 for the training set of 1196 compounds. For the test set of 300 compounds, the corresponding statistics was R 2 = 0.992. The results of this study showed that it would be successful to predict Δ H c o by using the easily calculated atom-type E-state indices, which can provide one more way for predicting the Δ H c o of organic compounds for engineering based on only their molecular structures.

Prediction of auto-ignition temperatures of hydrocarbons by neural network based on atom-type electrotopological-state indices

A quantitative structure–property relationship (QSPR) model was constructed to predict the auto-ignition temperature (AIT) of 118 hydrocarbons by means of artificial neural network (ANN). Atom-type electrotopological-state indices were used as molecular structure descriptors which combined together both electronic and topological characteristics of the analyzed molecules. The typical back-propagation (BP) neural network was employed for fitting the possible non-linear relationship existed between the atom-type electrotopological-state indices and AIT. The dataset of 118 hydrocarbons was randomly divided into a training set (60), a validation set (16) and a testing set (42). The optimal condition of the neural network was obtained by adjusting various parameters by trial-and-error. Simulated with the final optimum BP neural network [16-8-1], the results show that most of the predicted AIT values are in good agreement with the experimental data, with the average absolute error being 21.6 °C, and the root mean square error (RMS) being 31.09 for the testing set, which are superior to those obtained by multiple linear regression analysis and traditional group contribution method. The model proposed can be used not only to reveal the quantitative relation between AIT and molecular structures of hydrocarbons, but also to predict the AIT values of hydrocarbons for chemical engineering.

Prediction of soil sorption coefficient of organic pesticides from the atom‐type electrotopological state indices

A group contribution approach based on atom-type electrotopological state indices for predicting the soil sorption coefficient (log KOC) of a diverse set of 201 organic pesticides is presented. Using a training set of 143 compounds, for which the log KOC values were in the range from 0.42 to 5.31, multiple linear regression (MLR) and artificial neural networks were used to build the models. The models were validated using two test sets of 20 and 38 chemicals not included in the training set. The statistics for a linear model with 12 structural parameters were, in test set 1, r2 = 0.79, s = 0.45 and, in test set 2, r2 = 0.74, s = 0.65. These results clearly show that soil sorption coefficients can be accurately and rapidly estimated from easily calculated structural parameters.

PREDICTION OF SOIL SORPTION COEFFICIENT OF ORGANIC PESTICIDES FROM THE ATOM-TYPE ELECTROTOPOLOGICAL STATE INDICES

A group contribution approach based on atom-type electrotopological state indices for predicting the soil sorption coefficient (log KOC) of a diverse set of 201 organic pesticides is presented. Using a training set of 143 compounds, for which the log KOC values were in the range from 0.42 to 5.31, multiple linear regression (MLR) and artificial neural networks were used to build the models. The models were validated using two test sets of 20 and 38 chemicals not included in the training set. The statistics for a linear model with 12 structural parameters were, in test set 1, r2 = 0.79, s = 0.45 and, in test set 2, r2 = 0.74, s = 0.65. These results clearly show that soil sorption coefficients can be accurately and rapidly estimated from easily calculated structural parameters.

Development of Quantitative Structure−Property Relationship Models for Early ADME Evaluation in Drug Discovery. 2. Blood-Brain Barrier Penetration

A new molecular lipoaffinity descriptor was introduced in this paper to account for the effect of molecular hydrophobicity on blood-brain barrier penetration. The descriptor was defined based on Kier and Hall's atom-type electrotopological state indices. Its evaluation requires 2-D molecular bonding information only. A multiple linear regression equation using this descriptor and molecular weight reproduces the experimental logBB values of 55 training set compounds and 11 test set compounds satisfactorily with statistical parameters nearly identical to the best models based on polar surface area and ClogP. The results indicate that the lipoaffinity descriptor defined in this paper may be a significant descriptor for molecular transport properties across lipid bilayers.

Prediction of biodegradation from the atom‐type electrotopological state indices

A group contribution method based on atom-type electrotopological state indices for predicting the biodegradation of a diverse set of 241 organic chemicals is presented. Multiple linear regression and artificial neural networks were used to build the models using a training set of 172 compounds, for which the approximate time for ultimate biodegradation was estimated from the results of a survey of an expert panel. Derived models were validated by using a leave-25%-out method and against two test sets of 12 and 57 chemicals not included in the training set. The squared correlation coefficient (r2) for a linear model with 15 structural parameters was 0.76 for the training set and 0.68 for the test set of 12 molecules. The model predicted correctly the biodegradation of 48 chemicals in the test set of 57 molecules, for which biodegradability was presented as rapid or slow. The use of artificial neural networks gave better prediction for both test sets when the same set of parameters was tested as inputs in neural network simulations. The predictions of rapidly biodegradable chemicals were more accurate than the predictions of slowly biodegradable chemicals for both the regression and neural network models.

Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices

We describe robust methods for estimating the aqueous solubility of a set of 734 organic compounds from different structural classes based on multiple linear regression (MLR) and artificial neural networks (ANN) model. The structures were represented by atom-type electrotopological state (E-state) indices. The squared correlation coefficient and standard deviation for the MLR with 34 structural parameters were r 2=0.94 and s=0.58 for the training set of 675 compounds. For the test set of 21 compounds, the equivalent statistics were r 2 pred=0.80 and s=0.87, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.52 for the training set and s=0.75 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the estimation of aqueous solubility for a large and diverse set of organic compounds using easily calculated structural parameters.

Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indices

A method for predicting log P values for a diverse set of 1870 organic molecules has been developed based on atom-type electrotopological-state (E-state) indices and neural network modeling. An extended set of E-state indices, which included specific indices with a more detailed description of amino, carbonyl, and hydroxy groups, was used in the current study. For the training set of 1754 molecules the squared correlation coefficient and root-mean-squared error were r2 = 0.90 and RMS(LOO) = 0.46, respectively. Structural parameters which included molecular weight and 38 atom-type E-state indices were used as the inputs in 39-5-1 artificial neural networks. The results from multilinear regression analysis were r2 = 0.87 and RMS(LOO) = 0.55, respectively. For a test set of 35 nucleosides, 12 nucleoside bases, 19 drug compounds, and 50 general organic compounds (n = 116) not included in the training set, a predictive r2 = 0.94 and RMS = 0.41 were calculated by artificial neural networks. The results for the same set by multilinear regression were r2 = 0.86 and RMS = 0.72. The improved prediction ability of artificial neural networks can be attributed to the nonlinear properties of this method that allowed the detection of high-order relationships between E-state indices and the n-octanol/water partition coefficient. The present approach was found to be an accurate and fast method that can be used for the reliable estimation of log P values for even the most complex structures.

Boiling Point of a Set of Alkanes, Alcohols and Chloroalkanes: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

The normal boiling point is modeled for a set of 372 saturated compounds, including 154 alkanes, 108 alcohols and 110 (poly)chloroalkanes. The newly introduced atom type electrotopological state indices serve as the structure variables and artificial neural networks (with back propagation of error) are used for the analysis. A network with a 6:7:1 architecture produces an average relative error of 0.97% for the whole data set, including the 21% of the data used as the test set. The mean absolute error (MAE) for this model is 4.00 for the whole set, corresponding to an rms error of 5.41; for the test set the MAE is 4.03 with an rms of 5.23. The low error on the test set indicates that this model has predictive power.

ChemInform Abstract: Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Boiling Point and Critical Temperature of a Heterogeneous Data Set: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

Two sets of heterogeneous organic compounds were analyzed with artificial neural networks using atom type electrotopological state indices. The first set contains the boiling point for 298 compounds; 30 were placed in a testing set. The neural network model used atom type E-state indices for the 19 atom types present in the data set; the actual network used for prediction had a 19:5:1 architecture. This model produced a mean absolute error (MAE) of 3.93 K for the overall set, 3.86 for the training set, and 4.57 for the test set. The average relative percent error for 10 runs is 0.94% for the whole data set and 1.12% for the test set. The second set contains critical temperatures for 165 compounds; 18 were placed in the testing set. The neural network possessed a 19:4:1 architecture and produced an MAE of 4.52 K for the whole set, 4.39 K for the training set, and 5.59 K for the test set. The average relative percent error for 5 runs is 0.77% for the whole data set and 0.95% for the test set.