Breaking the polar‐nonpolar division in solvation free energy prediction
Breaking the polar‐nonpolar division in solvation free energy prediction
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DOI：
10.1002/jcc.25107
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发表时间：
2018-02
影响因子：
3
通讯作者：
Bao Wang;Chengzhang Wang;Kedi Wu;G. Wei
中科院分区：
文献类型：
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作者：
Bao Wang;Chengzhang Wang;Kedi Wu;G. Wei
隐式溶剂模型将溶剂化自由能分为极性和非极性添加剂贡献，而极性和非极性相互作用是不可分的和非添加剂。我们提出了一个功能理论（FFT）的框架，打破这种特设的分工。FFT的基本思想如下：（i）可表示性假设：存在可以唯一表征和区分一个分子与另一个分子的微观特征向量;（ii）特征函数关系假设：分子的宏观特征，包括溶剂化自由能，是微观特征向量的泛函;以及（iii）相似性假设：具有相似微观特征的分子具有相似的宏观性质，例如溶剂化自由能。基于这些假设，在以下方案中进行溶剂化自由能预测。首先，我们构建了一个分子微观特征向量，这是有效的，利用量子力学和Poisson-Boltzmann理论表征溶剂化过程。微观特征向量与宏观特征（即物理可观察到的特征）相结合，形成扩展特征向量。此外，我们根据分子组成将溶剂化数据集划分为查询。此外，对于每个目标分子，我们采用机器学习算法进行其最近邻搜索，基于选定的微观特征向量。最后，从得到的最近邻的扩展特征向量，我们构建了一个功能的溶剂化自由能，它被用来预测的目标分子的溶剂化自由能。所提出的FFT模型已经通过668个分子的大型数据集进行了广泛的验证。留一法检验给出的最佳均方根误差（RMSE）为1.05 kcal/mol。SAMPL 0、SAMPL 1、SAMPL 2、SAMPL 3和SAMPL 4挑战组的FFT预测分别提供0.61、1.86、1.64、0.86和1.14 kcal/mol的RMSE。使用测试集的94个分子及其相关的训练集，本方法进行了仔细比较与一个经典的溶剂化模型的基础上加权溶剂可及表面积。© 2017 Wiley Periodicals，Inc.
Implicit solvent models divide solvation free energies into polar and nonpolar additive contributions, whereas polar and nonpolar interactions are inseparable and nonadditive. We present a feature functional theory (FFT) framework to break this ad hoc division. The essential ideas of FFT are as follows: (i) representability assumption: there exists a microscopic feature vector that can uniquely characterize and distinguish one molecule from another; (ii) feature‐function relationship assumption: the macroscopic features, including solvation free energy, of a molecule is a functional of microscopic feature vectors; and (iii) similarity assumption: molecules with similar microscopic features have similar macroscopic properties, such as solvation free energies. Based on these assumptions, solvation free energy prediction is carried out in the following protocol. First, we construct a molecular microscopic feature vector that is efficient in characterizing the solvation process using quantum mechanics and Poisson–Boltzmann theory. Microscopic feature vectors are combined with macroscopic features, that is, physical observable, to form extended feature vectors. Additionally, we partition a solvation dataset into queries according to molecular compositions. Moreover, for each target molecule, we adopt a machine learning algorithm for its nearest neighbor search, based on the selected microscopic feature vectors. Finally, from the extended feature vectors of obtained nearest neighbors, we construct a functional of solvation free energy, which is employed to predict the solvation free energy of the target molecule. The proposed FFT model has been extensively validated via a large dataset of 668 molecules. The leave‐one‐out test gives an optimal root‐mean‐square error (RMSE) of 1.05 kcal/mol. FFT predictions of SAMPL0, SAMPL1, SAMPL2, SAMPL3, and SAMPL4 challenge sets deliver the RMSEs of 0.61, 1.86, 1.64, 0.86, and 1.14 kcal/mol, respectively. Using a test set of 94 molecules and its associated training set, the present approach was carefully compared with a classic solvation model based on weighted solvent accessible surface area. © 2017 Wiley Periodicals, Inc.