Journal
MOLECULAR PHARMACEUTICS
Volume 11, Issue 8, Pages 2962-+Publisher
AMER CHEMICAL SOC
DOI: 10.1021/mp500103r
Keywords
solubility; bioavailability; QSPR; QSAR; druglike; ADME; Random Forest; dissolution; experimental error; CheqSol; Noyes-Whitney; Henderson-Hasselbalch; polymorph; crystal; machine learning; general solubility equation; ADMET; pharmaceutical; rule-of-five
Funding
- European Commission through a Marie Curie Intra-European Fellowship - European Community
- University of Strathclyde
- EPSRC
- EPSRC [EP K0005861]
- Scottish Universities Life Sciences Alliance (SULSA)
- EPSRC [EP/K000195/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/K000195/1] Funding Source: researchfish
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We report the results of testing quantitative structure property relationships (QSPR) that were trained upon the same druglike molecules but two different sets of solubility data: (i) data extracted from several different sources from the published literature, for which the experimental uncertainty is estimated to be 0.6-0.7 log S units (referred to mol/L); (ii) data measured by a single accurate experimental method (CheqSol), for which experimental uncertainty is typically <0.05 log S units. Contrary to what might be expected, the models derived from the CheqSol experimental data are not more accurate than those derived from the noisy literature data. The results suggest that, at the present time, it is the deficiency of QSPR methods (algorithms and/or descriptor sets), and not, as is commonly quoted, the uncertainty in the experimental measurements, which is the limiting factor in accurately predicting aqueous solubility for pharmaceutical molecules.
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