The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

中文翻译：

---

评估 SAMPL6 第 II 部分 log P 挑战赛中辛醇-水分配系数预测的准确性。

SAMPL 挑战赛旨在让生物分子和物理建模界关注限制合理药物设计的蛋白质-配体结合预测模型准确性的问题。在 SAMPL5 log D 挑战赛中，旨在对预测类药物小分子从水相到非极性相的自由能转移的方法的准确性进行基准测试，参与者发现由于质子化态问题的复杂性，很难做出准确的预测。在 SAMPL6 log P 挑战赛中，我们要求参与者对 11 种化合物的中性物质的辛醇-水分配系数进行盲目预测，并评估这些方法在没有质子化态影响的复杂情况下的表现。该挑战以 SAMPL6 p[公式：参见文本] 挑战为基础，该挑战要求参与者预测本次 log P 挑战中考虑的化合物超集的 p[公式：参见文本] 值。从 27 个研究小组收集了 91 种预测方法的盲预测集，涵盖各种基于量子力学 (QM) 或分子力学 (MM) 的物理方法、基于知识的经验方法和混合方法。与 SAMPL5 log D 挑战赛相比，参与小组数量增加了 50%，提交数量增加了 20%。总体而言，SAMPL6 Challenge 中的辛醇-水 log P 预测的准确性高于 SAMPL5 中的环己烷-水 log D 预测，这可能是因为 log P 仅需要对中性物质进行建模，并且几类方法受益于大量实验辛醇-水测井 P 数据。有许多高精度方法：10 种不同的方法实现了 RMSE 小于 0.5 log P 单位。其中包括基于质量管理的方法、经验方法以及采用经验修正支持的物理建模的混合方法。物理建模方法的比较表明，基于 QM 的方法优于基于 MM 的方法。最准确的五种基于 MM、基于 QM、经验和基于 RMSE 的混合方法的平均 RMSE 分别为 0.92 ± 0.13、0.48 ± 0.06、0.47 ± 0.05 和 0.50 ± 0.06。