Within the framework of the 6th physical property blind challenge (SAMPL6) the authors have participated in predicting the octanol–water partition coefficients (logP) for several small drug like molecules. Those logP values where experimentally known by the organizers but only revealed after the submissions of the predictions. Two different sets of predictions were submitted by the authors, both based on the COSMOtherm implementation of COSMO-RS theory. COSMOtherm predictions using the FINE parametrization level (hmz0n) obtained the highest accuracy among all submissions as measured by the root mean squared error. COSMOquick predictions using a fast algorithm to estimate σ-profiles and an a posterio machine learning correction on top of the COSMOtherm results (3vqbi) scored 3rd out of 91 submissions. Both results underline the high quality of COSMO-RS derived molecular free energies in solution.

在第六次物理特性盲挑战（SAMPL6）的框架内，作者参与了预测几种小分子药物分子的辛醇-水分配系数（logP）的工作。那些logP值是组织者通过实验得知的，但仅在提交预测后才显示。作者提交了两组不同的预测，均基于COSMO-RS理论的COSMOtherm实现。使用FINE参数化级别（hmz0n）进行的COSMOtherm预测获得的所有提交中，以均方根均方根误差衡量的准确性最高。COSMOquick预测使用快速算法估算σ分布，并在COSMOtherm结果（3vqbi）的基础上进行后代机器学习校正，在91个提交中获得了第3名。