The aqueous solubility is predicted here using quantitative structure property relationship (QSPR) models. In this study, we examine whether descriptors that individually yield favorable models for the prediction of the Gibbs energy of solvation and sublimation can be used in combination with octanol-water partition coefficient to produce QSPR models for the prediction of aqueous solubility. Based on this strategy, applied to seven distinct datasets, all models exhibited an R² greater than 0.7 and Q² greater than 0.6 for the estimation of aqueous solubility. We also determined how uncoupling the descriptors used to create QSPR models in the prediction of Gibbs energy of sublimation yielded an improved model. Model refinement using an artificial neural network applying the same descriptors generated significantly better models with improved R² and standard deviation.

在此使用定量结构性质关系（QSPR）模型预测水溶性。在这项研究中，我们检查是否可以单独使用产生有利模型来预测溶剂化和升华的吉布斯能量的描述符与辛醇-水分配系数结合使用，以生成用于预测水溶性的QSPR模型。基于此策略，将其应用于七个不同的数据集，所有模型的R ^2均大于0.7，而Q ²大于0.6的水溶性估计值。我们还确定了如何在创建升华吉布斯能量的预测中解耦用于创建QSPR模型的描述符，从而获得改进的模型。使用应用相同描述符的人工神经网络对模型进行细化，可以生成具有改进的R ²和标准偏差的明显更好的模型。