Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.
Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.
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DOI：
10.1007/s10822-016-9954-8
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发表时间：
2016-11
影响因子：
3.5
通讯作者：
Mobley, David L.
中科院分区：
文献类型：
--
作者：
Bannan, Caitlin C.;Burley, Kalistyn H.;Chiu, Michael;Shirts, Michael R.;Gilson, Michael K.;Mobley, David L.
在最近的 SAMPL5 挑战中，参与者提交了对一组 53 个小分子的环己烷/水分配系数的预测。分配系数 (log D) 取代了过去五次 SAMPL 挑战的核心部分水合自由能。来自 18 个参与小组的 76 份提交材料代表了各种各样的计算方法。在这里，我们通过各种错误指标分析提交内容，并提供我们执行的许多参考计算的详细信息。与 SAMPL4 挑战一样，我们不仅评估了参与者的统计不确定性，还评估了模型不确定性的能力，即他们预测模型大小或特定预测的力场误差的能力。不幸的是，这仍然是预测和分析需要改进的领域。在 SAMPL4 中，表现最好的提交材料的均方根误差 (RMSE) 约为 1.5 kcal/mol。如果我们预计 log D 预测的准确性与 SAMPL4 中的水合自由能预测相似，则此处的预期误差将约为 1.54 log 单位。只有少数提交的预测 log D 值的 RMSE 低于 2.5 log 单位。然而，分布系数引入了过去 SAMPL 挑战中不存在的复杂性，包括互变异构体计数，这对于预测药物发现感兴趣的生物分子特性可能很重要，因此预计准确性会有所下降。总体而言，SAMPL5 分配系数挑战让我们深入了解了对各种物理效应进行建模的重要性。我们相信，这些类型的测量将成为未来盲目挑战的有前途的数据来源，特别是考虑到实验相对简单的性质和所提供的洞察力水平。
In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.
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影响因子：
5.5
作者：
Hess, Berk;Kutzner, Carsten;Lindahl, Erik
通讯作者：
Lindahl, Erik
适应半明确的组装溶剂化模型，以用Sampl5分子估算水环己烷分配。
影响因子：
3.5
作者：
Brini E;Paranahewage SS;Fennell CJ;Dill KA
通讯作者：
Dill KA
影响因子：
3.7
作者：
Garrido, Nuno M.;Economou, Ioannis G.;Macedo, Eugenia A.
通讯作者：
Macedo, Eugenia A.
影响因子：
3.5
作者：
Diaz-Rodriguez, Sebastian;Bozada, Samantha M.;Paluch, Andrew S.
通讯作者：
Paluch, Andrew S.
影响因子：
3.3
作者：
Garrido, Nuno M.;Jorge, Miguel;Macedo, Eugenia A.
通讯作者：
Macedo, Eugenia A.