Within the scope of SAMPL7 challenge for predicting physical properties, the Integral Equation Formalism of the Miertus-Scrocco-Tomasi (IEFPCM/MST) continuum solvation model has been used for the blind prediction of n-octanol/water partition coefficients and acidity constants of a set of 22 and 20 sulfonamide-containing compounds, respectively. The log P and pK_a were computed using the B3LPYP/6-31G(d) parametrized version of the IEFPCM/MST model. The performance of our method for partition coefficients yielded a root-mean square error of 1.03 (log P units), placing this method among the most accurate theoretical approaches in the comparison with both globally (rank 8th) and physical (rank 2nd) methods. On the other hand, the deviation between predicted and experimental pK_a values was 1.32 log units, obtaining the second best-ranked submission. Though this highlights the reliability of the IEFPCM/MST model for predicting the partitioning and the acid dissociation constant of drug-like compounds compound, the results are discussed to identify potential weaknesses and improve the performance of the method.

在预测物理性质的 SAMPL7 挑战范围内，Miertus-Scrocco-Tomasi (IEFPCM/MST) 连续溶剂化模型的积分方程形式主义已用于盲预测正辛醇/水分配系数和酸度常数。分别包含 22 和 20 种含磺酰胺的化合物。log P和 p K _a是使用 IEFPCM/MST 模型的 B3LPYP/6-31G(d) 参数化版本计算的。我们的分配系数方法的性能产生了 1.03 的均方根误差（log P个单位），在与全局（第 8 位）和物理（第 2 位）方法进行比较时，将该方法置于最准确的理论方法之列。另一方面，预测和实验 p K _a值之间的偏差为 1.32 个对数单位，获得排名第二的提交。虽然这突出了 IEFPCM/MST 模型在预测类药物化合物的分配和酸解离常数方面的可靠性，但对结果进行了讨论，以识别潜在的弱点并提高该方法的性能。