Computational aqueous solubility prediction for drug-like compounds in congeneric series

It was the aim of the present work to develop a quantitative structure–property relationship (QSPR) model for predicting the aqueous solubility of drug-like compounds in congeneric series. Lipophilicity combined with structural fragment information, fragmental based correction factors and congeneric series indices were used as descriptors for a principal component analysis (PCA) followed by multivariate partial least squares regression statistics (PLS). The derived PLS regression model for the prediction of solubility parameters was based on an in-house data set of 2473 drug-like compounds. The generated PLS model had a coefficient of determination (R2) = 0.844 and a root-mean-square (rms) error of 0.51 log units. It predicted the solubility of the test data set with a high degree of accuracy (R2 = 0.81). In addition, the PLS model was successful in predicting the solubility of new congeneric test sets when solubility values of corresponding scaffolds were accessible.

It was the aim of the present work to develop a quantitative structure–property relationship (QSPR) model for predicting the aqueous solubility of drug-like compounds in congeneric series. Lipophilicity combined with structural fragment information, fragmental based correction factors and congeneric series indices were used as descriptors for a principal component analysis (PCA) followed by multivariate partial least squares regression statistics (PLS). The derived PLS regression model for the prediction of solubility parameters was based on an in-house data set of 2473 drug-like compounds. The generated PLS model had a coefficient of determination (R2) = 0.844 and a root-mean-square (rms) error of 0.51 log units. It predicted the solubility of the test data set with a high degree of accuracy (R2 = 0.81). In addition, the PLS model was successful in predicting the solubility of new congeneric test sets when solubility values of corresponding scaffolds were accessible.Figure optionsDownload as PowerPoint slide