Inspired by the successful application of the embedded cluster reference interaction site model (EC-RISM), a combination of quantum–mechanical calculations with three-dimensional RISM theory to predict Gibbs energies of species in solution within the SAMPL6.1 (acidity constants, pK_a) and SAMPL6.2 (octanol–water partition coefficients, log P) the methodology was applied to the recent SAMPL7 physical property challenge on aqueous pK_a and octanol–water log P values. Not part of the challenge but provided by the organizers, we also computed distribution coefficients log D_7.4 from predicted pK_a and log P data. While macroscopic pK_a predictions compared very favorably with experimental data (root mean square error, RMSE 0.72 pK units), the performance of the log P model (RMSE 1.84) fell behind expectations from the SAMPL6.2 challenge, leading to reasonable log D_7.4 predictions (RMSE 1.69) from combining the independent calculations. In the post-submission phase, conformations generated by different methodology yielded results that did not significantly improve the original predictions. While overall satisfactory compared to previous log D challenges, the predicted data suggest that further effort is needed for optimizing the robustness of the partition coefficient model within EC-RISM calculations and for shaping the agreement between experimental conditions and the corresponding model description.

受嵌入式簇参考相互作用位点模型 (EC-RISM) 成功应用的启发，量子力学计算与三维 RISM 理论相结合，可预测 SAMPL6.1 溶液中物质的吉布斯能量（酸度常数，p Ka ₎和 SAMPL6.2（辛醇-水分配系数，log P ）该方法应用于最近针对水性_{p Ka}和辛醇-水 log P值的 SAMPL7 物理性质挑战。这不是挑战的一部分，但由组织者提供，我们还根据预测的 p K _a和 log P数据计算了分布系数 log D _7.4 。虽然宏观 p K _a预测与实验数据相比非常有利（均方根误差，RMSE 0.72 p K单位），但 log P模型的性能 (RMSE 1.84) 落后于 SAMPL6.2 挑战的预期，导致合理的对数结合独立计算得出的D _7.4预测（RMSE 1.69）。在提交后阶段，不同方法生成的构象产生的结果并没有显着改善原始预测。虽然与之前的 log D挑战相比总体令人满意，但预测数据表明，需要进一步努力优化 EC-RISM 计算中分配系数模型的鲁棒性，以及形成实验条件和相应模型描述之间的一致性。