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.
| Original language | English |
|---|---|
| Journal | Journal of Chemical Information and Modeling |
| Volume | 46 |
| Issue number | 6 |
| Pages (from-to) | 2601-2609 |
| Number of pages | 9 |
| ISSN | 1549-9596 |
| DOIs | |
| Publication status | Published - Nov 2006 |
Keywords
- 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