QSPR Prediction of Aqueous Solubility of Drug-Like Organic Compounds

Abstract

A quantitative structure property relationship (QSPR) study was performed to develop a model that relates the structures of 150 drug organic compounds to their aqueous solubility (log S(w)). Molecular descriptors derived solely from 3D structure were used to represent molecular structures. A subset of the calculated descriptors selected using stepwise regression that used in the QSPR model development. Multiple linear regression (MLR) is utilized to construct the linear QSPR model. The applied multiple linear regression is based on a variety of theoretical molecular descriptors selected by the stepwise variable subset selection procedure. Stepwise regression was employed to develop a regression equation based on 110 training compounds, and predictive ability was tested on 40 compounds reserved for that purpose. The final regression equation included three parameters that consisted of octanol/water partition coefficient (log P), molecular volume (MV) and hydrogen bond forming ability (HB), of the drug molecules, all of which could be related to solubility property. Application of the developed model to a testing set of 40 drug organic compounds demonstrates that the new model is reliable with good predictive accuracy and simple formulation. The use of descriptors calculated only from molecular structure eliminates the need for experimental determination of properties for use in the correlation and allows for the estimation of aqueous solubility for molecules not yet synthesized. The prediction results are in good agreement with the experimental values. The root mean square error of prediction (RMSEP) and square correlation coefficient (R(2)) of prediction of log S(w) were 0.0959 and 0.9954, respectively.