Prediction of pH-dependent aqueous solubility of druglike molecules

被引:74
作者
Hansen, Niclas Tue
Kouskoumvekaki, Irene
Jorgensen, Flemming Steen
Brunak, Soren
Jonsdottir, Svava Osk [1 ]
机构
[1] Tech Univ Denmark, Ctr Biol Sequence Anal, Bioctr, DK-2800 Lyngby, Denmark
[2] Danish Univ Pharmaceut Sci, Dept Med Chem, DK-2100 Copenhagen, Denmark
关键词
D O I
10.1021/ci600292q
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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 pK(a) 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.
引用
收藏
页码:2601 / 2609
页数:9
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