a Department of Computer-Aided Molecular Design , Russian Academy of Science , Chernogolovka , Russia.
b School of Pharmacy and Biomolecular Sciences , Liverpool John Moores University , Liverpool , UK.
SAR QSAR Environ Res. 2017 Aug;28(8):661-676. doi: 10.1080/1062936X.2017.1368704. Epub 2017 Sep 11.
Aqueous solubility at pH = 7.4 is a very important property for medicinal chemists because this is the pH value of physiological media. The present work describes the application of three different methods (support vector machine (SVM), random forest (RF) and multiple linear regression (MLR)) and three local quantitative structure-property relationship (QSPR) models (regression corrected by nearest neighbours (RCNN), arithmetic mean property (AMP) and local regression property (LoReP)) to construct stable QSPRs with clear mechanistic interpretation. Our data set contained experimental values of aqueous solubility at pH = 7.4 of 387 chemicals (349 in the training set and 38 in the test set including 16 own measurements). The initial descriptor pool contained 210 physicochemical descriptors, calculated from the HYBOT, DRAGON, SYBYL and VolSurf+ programs. Six QSPRs with good statistics based on fundamentals of aqueous solubility and optimization of descriptor space were obtained. Those models have an RMSE close to experimental error (0.70), and are amenable to physical interpretation. The QSPR models developed in this study may be useful for medicinal chemists. Global MLR, RF and SVM models may be valuable for consideration of common factors that influence solubility. The RCNN, AMP and LoReP local models may be helpful for the optimization of aqueous solubility in small sets of related chemicals.
在 pH=7.4 时的水溶解度是药物化学家非常重要的性质，因为这是生理介质的 pH 值。本工作描述了三种不同方法（支持向量机 (SVM)、随机森林 (RF) 和多元线性回归 (MLR)）和三种局部定量构效关系 (QSPR) 模型（通过最近邻回归校正的回归 (RCNN)、算术平均性质 (AMP) 和局部回归性质 (LoReP)）的应用，以构建具有明确机制解释的稳定 QSPR。我们的数据集中包含了 387 种化学物质在 pH=7.4 时的水溶解度的实验值（训练集中有 349 种，测试集中有 38 种，包括 16 种自有测量值）。初始描述符池包含了 210 个物理化学描述符，这些描述符是从 HYBOT、DRAGON、SYBYL 和 VolSurf+ 程序中计算出来的。基于水溶解度的基本原理和描述符空间的优化，得到了六个具有良好统计学意义的 QSPR。这些模型的 RMSE 接近实验误差（0.70），并且易于进行物理解释。本研究中开发的 QSPR 模型可能对药物化学家有用。全局 MLR、RF 和 SVM 模型可能有助于考虑影响溶解度的共同因素。RCNN、AMP 和 LoReP 局部模型可能有助于优化相关化学物质小数据集的水溶解度。
