A multiple linear regression model called MLR-3 is used for predicting the experimental n-octanol/water partition coefficient (log P_N) of 22 N-sulfonamides proposed by the organizers of the SAMPL7 blind challenge. The MLR-3 method was trained with 82 molecules including drug-like sulfonamides and small organic molecules, which resembled the main functional groups present in the challenge dataset. Our model, submitted as “TFE-MLR”, presented a root-mean-square error of 0.58 and mean absolute error of 0.41 in log P units, accomplishing the highest accuracy, among empirical methods and also in all submissions based on the ranked ones. Overall, the results support the appropriateness of multiple linear regression approach MLR-3 for computing the n-octanol/water partition coefficient in sulfonamide-bearing compounds. In this context, the outstanding performance of empirical methodologies, where 75% of the ranked submissions achieved root-mean-square errors < 1 log P units, support the suitability of these strategies for obtaining accurate and fast predictions of physicochemical properties as partition coefficients of bioorganic compounds.

称为 MLR-3 的多元线性回归模型用于预测SAMPL7 盲目挑战的组织者提出的22 N -磺酰胺的实验正辛醇/水分配系数 (log P _{N )。}MLR-3 方法使用 82 个分子进行训练，包括类药物磺酰胺和小有机分子，这些分子类似于挑战数据集中存在的主要官能团。我们的模型提交为“TFE-MLR”，在 log P单位中均方根误差为 0.58，平均绝对误差为 0.41，在经验方法以及基于排名的所有提交的方法中实现了最高的准确度。总体而言，结果支持多元线性回归方法 MLR-3 用于计算含磺酰胺化合物中正辛醇/水分配系数的适当性。在这种情况下，经验方法的出色表现，其中 75% 的排名提交的均方根误差 < 1 log P单位，支持这些策略对于获得准确和快速的物理化学性质预测的适用性，因为分配系数为生物有机化合物。