QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors

Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.

An Electronic-structure Informatics Study on the Toxicity of Alkylphenols to Tetrahymena pyriformis

Alkylphenols (APs) dissolved in water are known to be toxic to animals including humans. In this study, regression models describing the toxicity of the 33 AP molecules were investigated for reproducing and thereby making it possible to predict a quantitative structure-activity relationship (QSAR). For this purpose, we tried to derive regression models for the experimental IGC₅₀ (growth inhibition concentration at 50 %) to Tetrahymena pyriformis by using various descriptor sets consisting of electronic and shape descriptors. By applying the multiple linear regression (MLR) analysis, it was successful to derive a quantitative regression model for the IGC₅₀ values. In this analysis, the size parameters of the molecules were found important, suggesting that bulky molecules should be less toxic. We could also obtain, when the size descriptors were excluded, an MLR model indicating that the electron affinity (EA) should be important, which is consistent with the previous QSAR studies. Through the correlation analysis among the descriptors, it was shown that, in the present set of molecules, EA and a size parameter are highly correlated. Since EA was calculated to be negative, indicating that the related process would be energetically unfavourable, it was concluded that the size of the molecules should be a dominant factor determining IGC₅₀ . This implies that a molecular recognition process would play a critical role in the mode of action for the toxicity.

Prediction of Biodegradability for Polycyclic Aromatic Hydrocarbons Using Various In Silico Modeling Methods

Polycyclic aromatic hydrocarbons (PAHs) have attracted great concern as global environmental pollutants. In this work, the quantitative structure-biodegradability relationship (QSBR) study has been done to predict the biodegradability of PAHs and develop the correlation between the biodegradability and the molecular structures. The structural chemistry and quantum chemistry descriptors were used to represent molecular structures. Three in silico modeling methods, i.e., multiple linear regression (MLR), radial basis function neural network, and back-propagation artificial neural network (BPANN), are utilized to construct the linear and nonlinear prediction models and provide some insights into the structural characteristics affecting the biodegradability of PAHs. The stability of these QSBR models was tested by leave-one-out cross-validation, and the cross-validated correlation coefficients (q²) were 0.6109, 0.6887, and 0.6586, respectively. The correlation coefficients (R²) of the three models for the training set were 0.7811, 0.8883, and 0.9667, respectively. The comparison of the three models showed that the BPANN model produced a statistically more significant model than the other two models. On the basis of molecular structure, the dominant molecular structure descriptor affecting biodegradability of PAHs were analyzed and discussed.

In silico prediction of toxicity of non-congeneric industrial chemicals using ensemble learning based modeling approaches

Ensemble learning approach based decision treeboost (DTB) and decision tree forest (DTF) models are introduced in order to establish quantitative structure-toxicity relationship (QSTR) for the prediction of toxicity of 1450 diverse chemicals. Eight non-quantum mechanical molecular descriptors were derived. Structural diversity of the chemicals was evaluated using Tanimoto similarity index. Stochastic gradient boosting and bagging algorithms supplemented DTB and DTF models were constructed for classification and function optimization problems using the toxicity end-point in T. pyriformis. Special attention was drawn to prediction ability and robustness of the models, investigated both in external and 10-fold cross validation processes. In complete data, optimal DTB and DTF models rendered accuracies of 98.90%, 98.83% in two-category and 98.14%, 98.14% in four-category toxicity classifications. Both the models further yielded classification accuracies of 100% in external toxicity data of T. pyriformis. The constructed regression models (DTB and DTF) using five descriptors yielded correlation coefficients (R(2)) of 0.945, 0.944 between the measured and predicted toxicities with mean squared errors (MSEs) of 0.059, and 0.064 in complete T. pyriformis data. The T. pyriformis regression models (DTB and DTF) applied to the external toxicity data sets yielded R(2) and MSE values of 0.637, 0.655; 0.534, 0.507 (marine bacteria) and 0.741, 0.691; 0.155, 0.173 (algae). The results suggest for wide applicability of the inter-species models in predicting toxicity of new chemicals for regulatory purposes. These approaches provide useful strategy and robust tools in the screening of ecotoxicological risk or environmental hazard potential of chemicals.

Consensus ranking approach to understanding the underlying mechanism with QSAR

Constructing a highly predictive model and exploiting the underlying mechanism associated with a specific property of chemicals are the two main goals of quantitative structure-activity relationship analysis (QSAR). However, the latter has long been carried out as a byproduct of model construction. Here we confirmed for the first time in this study that conventional descriptor selection methods designed to develop a best predictive model are likely not suitable for mechanistic analysis, i.e., the selected descriptors strongly depended on the selection of chemicals in the training sets. As an alternative, a consensus ranking protocol was proposed to select a robust descriptor set for mechanistic analysis, which can successfully overcome the above shortcoming. Moreover, the consistently inferior model performance using descriptors selected for mechanistic analysis suggested the irreplaceable role of model development in achieving models with the best predictive capability.