posted on 2025-01-24, 02:40authored byNastaran Meftahi, Michael Walker, Brian SmithBrian Smith
The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R2 greater than 0.7 and Q2 greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R2 and standard deviation.
Funding
This work was supported by a Discovery Project grant from the Australian Research Council, DP130100998, and from the VLSCI's Life Sciences Computation Initiative, a collaboration between The University of Melbourne, Monash University and La Trobe University, and an initiative of the Victorian Government, Australia.