Structure–activity relationship study of trifluoromethylketone inhibitors of insect juvenile hormone esterase: Comparison of several classification methods

<div><p>Juvenile hormone esterase (JHE) plays a key role in the development and metamorphosis of holometabolous insects. Its inhibitors could possibly be targeted for insect control. Conversely, JHE may also be involved in endocrine disruption by xenobiotics, resulting in detrimental effects in beneficial insects. There is therefore a need to know the structural characteristics of the molecules able to monitor JHE activity, and to develop SAR and QSAR studies to estimate their effectiveness. For a large diverse population of 181 trifluoromethylketones (TFKs) – the most potent JHE inhibitors known to date – we recently proposed a binary classification (active/inactive) using a support vector machine and Codessa structural descriptors. We have now examined, using the same data set and with the same descriptors, the applicability and performance of five other machine learning approaches. These have been shown able to handle high dimensional data (with descriptors possibly irrelevant or redundant) and to cope with complex mechanisms, but without delivering explicit directly exploitable models. Splitting the data into five batches (training set 80%, test set 20%) and carrying out leave-one-out cross-validation, led to good results of comparable performance, consistent with our previous support vector classifier (SVC) results. Accuracy was greater than 0.80 for all approaches. A reduced set of 15 descriptors common to all the investigated approaches showed good predictive ability (confirmed using a three-layer perceptron) and gives some clues regarding a mechanistic interpretation.</p></div

Linear and non-linear modelling of the cytotoxicity of TiO₂ and ZnO nanoparticles by empirical descriptors

<div><p>Titanium oxide (TiO₂) and zinc oxide (ZnO) nanoparticles are among the most widely used in different applications in daily life. In this study, local regression and classification models were developed for a set of ZnO and TiO₂ nanoparticles tested at different concentrations for their ability to disrupt the lipid membrane in cells. Different regression techniques were applied and compared by checking the robustness of the models and their external predictive ability. Additionally, a simple classification model was developed, which predicts the potential for disruption of the studied nanoparticles with good accuracy (overall accuracy, specificity, and sensitivity >80%) on the basis of two empirical descriptors. The present study demonstrates that empirical descriptors, such as experimentally determined size and tested concentrations, are relevant to modelling the activity of nanoparticles. This information may be useful to screen the potential for harmful effect of nanoparticles in different experimental conditions and to optimize the design of toxicological tests. Results from the present study are useful to support and refine the future application of <i>in silico</i> tools to nanoparticles, for research and regulatory purposes.</p></div

QSAR models for predicting the toxicity of piperidine derivatives against <i>Aedes aegypti</i>

<p>QSAR models are proposed for predicting the toxicity of 33 piperidine derivatives against <i>Aedes aegypti</i>. From 2D topological descriptors, calculated with the PaDEL software, ordinary least squares multilinear regression (OLS-MLR) treatment from the QSARINS software and machine learning and related approaches including linear and radial support vector machine (SVM), projection pursuit regression (PPR), radial basis function neural network (RBFNN), general regression neural network (GRNN) and <i>k</i>-nearest neighbours (<i>k</i>-NN), led to four-variable models. Their robustness and predictive ability were evaluated through both internal and external validation. Determination coefficients (<i>r</i>²) greater than 0.85 on the training sets and 0.8 on the test sets were obtained with OLS-MLR and linear SVM. They slightly outperform PPR, radial SVM and RBFNN, whereas GRNN and <i>k</i>-NN showed lower performance. The easy availability of the involved structural descriptors and the simplicity of the MLR model make the corresponding model attractive at an exploratory level for proposing, from this limited dataset, guidelines in the design of new potentially active molecules.</p