Prediction of pH-dependent aqueous solubility of druglike molecules

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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.
Original languageEnglish
JournalJournal of Chemical Information and Modeling
Issue number6
Pages (from-to)2601-2609
Number of pages9
Publication statusPublished - 2012

    Research areas

  • 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

ID: 14147481