Prediction of pH-Dependent Aqueous Solubility of Druglike Molecules
Niclas Tue Hansen
Irene Kouskoumvekaki
Flemming Steen Jørgensen
Søren Brunak
Svava Ósk Jónsdóttir
10.1021/ci600292q.s002
https://acs.figshare.com/articles/journal_contribution/Prediction_of_pH_Dependent_Aqueous_Solubility_of_Druglike_Molecules/3045172
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<i> S</i>-units, while the Marvin p<i>K</i><sub>a</sub> 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<i>
S</i>-units. Finally, the combined model has been applied on profiling the solubility space at low pH of five
large vendor libraries.
2006-11-27 00:00:00
tool Marvin
drug candidates
HH
prediction method
Druglike MoleculesIn
solubility space
Marvin pKa
RMSE
PHYSPROP database
solubility predictions
467 molecules
27 drugs
log
druglike PHYSPROP subset
vendor libraries
4548 compounds
model
validation
square error