Abstract:
: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_name
J Chem Inf Modeljournal_title
Journal of chemical information and modelingauthors
Hansen NT,Kouskoumvekaki I,Jørgensen FS,Brunak S,Jónsdóttir SOdoi
10.1021/ci600292qsubject
Has Abstractpub_date
2006-11-01 00:00:00pages
2601-9issue
6eissn
1549-9596issn
1549-960Xjournal_volume
46pub_type
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