Prediction of pH-dependent aqueous solubility of druglike molecules.
Niclas Tue Hansen, Irene Kouskoumvekaki, Flemming Steen
Søren Brunak and Svava Ósk Jónsdóttir.
J Chem Inf Model: 46(6): 2601-9, 2006.
Center for Biological Sequence Analysis, Department of Systems Biology,
Technical University of Denmark, DK-2800 Lyngby, Denmark
1Danish University of Pharmaceutical Sciences,
Universitetsparken 2, DK-2100 Copenhagen, Denmark
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