The n-octanol/buffer solution distribution coefficient (or n-octanol/water partition coefficient) is of critical importance for measuring lipophilicity of drug candidates. After 4885 molecular descriptor generation, 15 molecular descriptors were selected to develop quantitative structure–property relationship (QSPR) models for distribution coefficients at pH 7.4 (log D _7.4) of a large data set consisting of 1043 organic compounds, which was divided into a training set (600 compounds) and a test set (443 compounds). Support vector machine (SVM) based on genetic algorithm was used to develop a model for log D _7.4 that has coefficient of determination r ² of 0.919 for the training set and 0.893 for the test set. The results suggest that the SVM model is accurate in predicting log D _7.4.

所述Ñ辛醇/缓冲溶液分配系数（或ñ辛醇/水分配系数）是至关重要的，用于测量候选药物的亲脂性。在生成了4885个分子描述符后，选择了15个分子描述符来建立定量结构-属性关系（QSPR）模型，以计算由1043种有机化合物组成的大型数据集在pH 7.4（log D _7.4）下的分布系数。一套（600种化合物）和一套测试（443种化合物）。使用基于遗传算法的支持向量机（SVM）开发具有确定系数r ^2的log D _7.4模型 训练集为0.919，测试集为0.893。结果表明，SVM模型在预测log D _7.4方面是准确的。