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

Abstract

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.

In silico prediction of the inhibition of new molecules on SARS-CoV-2 3CL protease by using QSAR: PSOSVR approach

“…The validation criteria in Table 2 for this model showed its weak ability to predict with relatively low R and R 2 values (under the accepted value of R 2 < 0.6). The table indicates that the MLR model failed to pass the threshold value for r¯ m 2 (> 0.5) and ∆r m 2 (< 0.2) (Ojha et al 2011 ; Euldji et al 2022 ). Fig.S-1 shows the regression plot for the train and test sets, where the predicted values do not corroborate with the experimental values.…”

“…The values of Q 2 , r¯ m 2 and ∆r m 2 of this model also satisfy the threshold value. The value of R 2 -Q 2 for the whole set (both training and test) 0.9650–0.9649 is 0.0001 (accepted value < 0.3), indicating that the model is not overfitted (Euldji et al 2022 ). Table 2 shows the statistical parameters for the PSO-SVR model.…”

Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

“…[ [48, 49] ] In the present work, Garson's method was used to split the hidden output connection weights into components associated with each input neuron using absolute connection weight values. as well as the relevance factor r, which is in the range of −1 to +1 and is given by the following equation [22]: where X k,i is the i th importing parameter, Y i is the i th exporting value, is the average value of the k th input, is the average value of exporting parameter, and n is the number of sets.…”

“…The modeling of the properties of ILs is mainly based on the use of equations of state and the application of machine learning algorithms. These algorithms show that they have various applications in different fields such as the medical [17], the electrical and electronic engineering [18], the petrochemical [19, 20], chemical engineering [21, 22] and the civil and environmental engineering [23]. Among the alternative methods of computational intelligence, are the ANN, the LSSVM, and the SVM fine tuning with particle swarm optimization (SVM‐PSO) are robust and accurate predictive methods that have recently been successfully applied for the prediction of various properties [6].…”

“…The modeling of the properties of ILs is mainly based on the use of equations of state and the application of machine learning algorithms. These algorithms show that they have various applications in different fields such as the medical [17], the electrical and electronic engineering [18], the petrochemical [19,20], chemical engineering [21,22] and the civil and environmental engineering [23].…”