Palmer David S, Mitchell John B O
Department of Chemistry, University of Strathclyde , Thomas Graham Building, 295 Cathedral Street, Glasgow, Scotland G1 1XL, U.K.
Mol Pharm. 2014 Aug 4;11(8):2962-72. doi: 10.1021/mp500103r. Epub 2014 Jul 9.
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.
我们报告了对定量结构-性质关系（QSPR）进行测试的结果，这些关系是基于相同的类药物分子，但采用了两组不同的溶解度数据进行训练：（i）从已发表文献的几个不同来源提取的数据，其实验不确定性估计为0.6 - 0.7 log S单位（以mol/L计）；（ii）通过单一精确实验方法（CheqSol）测量的数据，其实验不确定性通常<0.05 log S单位。与预期相反，从CheqSol实验数据得出的模型并不比从“有噪声”的文献数据得出的模型更准确。结果表明，目前，限制准确预测药物分子水溶性的因素是QSPR方法（算法和/或描述符集）的不足，而不是如通常所说的实验测量中的不确定性。
