Prediction of pH-dependent aqueous solubility of druglike molecules
Research output: Contribution to journal › Journal article › Research › peer-review
In the present work, the Henderson-Hasselbalch (HH) equation has been employed for the development of a tool for the prediction of pH-dependent aqueous solubility of drugs and drug candidates. A new prediction method for the intrinsic solubility was developed, based on artificial neural networks that have been trained on a druglike PHYSPROP subset of 4548 compounds. For the prediction of acid/base dissociation coefficients, the commercial tool Marvin has been used, following validation on a data set of 467 molecules from the PHYSPROP database. The best performing network for intrinsic solubility predictions has a cross-validated root mean square error (RMSE) of 0.70 log S-units, while the Marvin pKa plug-in has an RMSE of 0.71 pH-units. A data set of 27 drugs with experimentally determined pH-solubility curves was assembled from the literature for the validation of the combined pH-dependent model, giving a mean RMSE of 0.79 log S-units. Finally, the combined model has been applied on profiling the solubility space at low pH of five large vendor libraries.
|Journal||Journal of Chemical Information and Modeling|
|Number of pages||9|
|Publication status||Published - 2012|
- Chemistry, Pharmaceutical, Crystallization, Databases as Topic, Drug Design, Hydrogen-Ion Concentration, Models, Chemical, Models, Statistical, Models, Theoretical, Neural Networks (Computer), Pharmaceutical Preparations, Software, Solubility, Solvents, Technology, Pharmaceutical, Water