The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pK_a for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pK_a challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pK_a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK_a values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pK_a prediction methods.

蛋白质和配体建模的统计评估 (SAMPL) 挑战将计算建模社区的重点放在需要改进合理药物设计的领域。SAMPL7 物理性质挑战涉及预测 22 种化合物的辛醇-水分配系数和 p K _a。该数据集由一系列 N-酰基磺酰胺和相关的生物等排体组成。17个研究组参与了log  P挑战，共提交33个盲投。p K _a挑战赛共有 7 个不同的组参与，共提交 9 个盲投。总体而言，SAMPL7 挑战中辛醇-水 log  P预测的准确性低于辛醇-水 log  PSAMPL6 中的预测，可能是由于数据集更加多样化。与 SAMPL6 p K _a挑战相比，SAMPL7 的准确性保持不变。有趣的是，在这里，虽然宏观 p K _a值通常以合理的准确度进行预测，但参与者之间对于哪些微观跃迁产生这些值的分歧明显更大（即使对于与某些相关的自由能变化的符号，方法也经常存在分歧）转换），表明在 p K _a预测方法上需要做更多的工作。