Mol Inform. 2022 Apr 3. doi: 10.1002/minf.202200026. Online ahead of print.
ABSTRACT
A quantitative structure-property relationship (QSPR) modeling was carried out for predicting drug and drug-like compounds solubility in supercritical carbon dioxide. For the first time, a dataset of 148 drugdrug-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 approached (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug are published as a function of temperature and pressure. In this study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3s, nBondsM, AVP-0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimised model was found to be {13,10,1}. Validation of the model was checked using several recommended 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). The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirm that the optimised ANN-QSPR model can be used reliably for the correlation and prediction of this property.
PMID:35373477 | DOI:10.1002/minf.202200026