LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules
LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules
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DOI：
10.1007/s10822-020-00278-1
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发表时间：
2020-05-01
影响因子：
3.5
通讯作者：
Matthews, Slade
中科院分区：
文献类型：
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作者：
Guan, Davy;Lui, Raymond;Matthews, Slade
这项工作提出了一个量子力学模型预测辛醇-水分配系数的小蛋白激酶抑制剂片段的SAMPL 6 LogP预测挑战的一部分。该模型使用具有SMD隐式溶剂化的M06-2X泛函和def 2-SVP基组计算溶剂化自由能差。该模型在SAMPL 6挑战中被确定为dqxk 4，并且是物理方法类别中性能第三高的模型，具有0.49 log均方根误差（RMSE），用于预测SAMPL 6盲预测集中的11种化合物。我们还合作研究了经验模型的使用，以最小的额外计算成本来解决卤化化合物的模型缺陷。由dqxk 4物理模型和hdpuj经验模型组成的混合模型在SAMPL 6数据集上发现了0.34 log RMSE的改进性能。这种合作的混合模型方法显示了如何利用经验模型来方便地提高从头计算方法难以模拟的化学空间的性能。
This work presents a quantum mechanical model for predicting octanol-water partition coefficients of small protein-kinase inhibitor fragments as part of the SAMPL6 LogP Prediction Challenge. The model calculates solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set. This model was identified as dqxk4 in the SAMPL6 Challenge and was the third highest performing model in the physical methods category with 0.49 log Root Mean Squared Error (RMSE) for predicting the 11 compounds in SAMPL6 blind prediction set. We also collaboratively investigated the use of empirical models to address model deficiencies for halogenated compounds at minimal additional computational cost. A mixed model consisting of the dqxk4 physical and hdpuj empirical models found improved performance at 0.34 log RMSE on the SAMPL6 dataset. This collaborative mixed model approach shows how empirical models can be leveraged to expediently improve performance in chemical spaces that are difficult for ab initio methods to simulate.