The trained versions of Yalkowsky and Jouyban-Acree models are proposed to predict solubility of drugs in binary aqueous mixtures of N-methyl-2-pyrrolidone (NMP) at various temperatures. To provide a full predictive model, the Abraham solvation parameters of solutes are combined with the Jouyban-Acree model and Jouyban-Acree-van't Hoff model. Since this investigation includes a number of compounds with different polarities and structural features, the results may provide accurate estimations of solubilization for most compounds of interest. The overall mean relative deviation (MRD) values for the back-calculated solubility of drugs in {NMP + water} solvent mixtures are 20.9–39.3% for the proposed models indicating that the generally trained models provided acceptable predictions and could be helpful in the pharmaceutical industry.

提出了经过训练的Yalkowsky和Jouyban-Acree模型，以预测药物在不同温度下在N-甲基-2-吡咯烷酮（NMP）的二元水性混合物中的溶解度。为了提供完整的预测模型，将溶质的亚伯拉罕溶剂化参数与Jouyban-Acree模型和Jouyban-Acree-van't Hoff模型结合使用。由于此研究包括许多具有不同极性和结构特征的化合物，因此该结果可为大多数目标化合物提供准确的增溶估计。总体平均相对偏差（MRD）对于拟议模型，药物在{NMP +水}溶剂混合物中的反算溶解度值是20.9–39.3％，表明一般训练的模型提供了可接受的预测，并且可能对制药业有所帮助。