Hybrid Improved Grey Wolf Support Vector Regression Algorithm for Modeling Solubilities of APIs in Pure Ionic Liquids: σ-Profile Descriptors.

The objective of this study was to model the solubility of active pharmaceutical ingredients (APIs) in different ionic liquids (ILs) based on the σ-moments of cations, anions, and APIs that were used as molecular descriptors calculated using the σ-profiles of three categories of descriptors based on conductor-like screening model for real solvents. The database of 83 API-ILs systems composed of 14 APIs, 12 cations, and 7 anions (25 ILs combinations) was collected as 850 data points at different temperature ranges. A hybrid Improved Grey Wolf Support vector regression, abbreviated as I-GWO-SVR(r), algorithm was selected as the learning method. Based on a comprehensive comparison with 11 different models, various statistical factors, and graphical analyses, including an external validation test, analysis of variance (ANOVA), and sensitivity analysis, the capability and validity of the proposed approach have been assessed and verified. The overall study confirmed that the proposed new model provided the best results in terms of predicting the solubility of APIs in ILs.

QSPR Modelling of the Solubility of Drug and Drug-like Compounds in Supercritical Carbon Dioxide.

Quantitative structure-property relationship (QSPR) modeling was investigated to predict drug and drug-like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug-like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP-0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2 =0.9528), and robustise (Q2 =0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN-QSPR model is suitable for the correlation and prediction of this property.

A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.