Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology
Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology
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DOI：
10.1021/ci9901338
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发表时间：
2000-05-01
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影响因子：
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通讯作者：
Huuskonen, J
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文献类型：
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作者：
Huuskonen, J
提出了一种基于多元线性回归和人工神经网络模型的1297种有机化合物水溶液溶解度估算方法，该方法具有较高的精度和通用性。用分子连接性指数、形状指数和原子型电拓扑态指数作为结构参数。数据集被分为884个化合物的训练集和413个化合物的随机选择的测试集。30-12-1人工神经网络的结构参数包括24个原子型E态指数和6个其他拓扑指数，对测试集的预测r(2)=0.92，S=0.60。在相同参数下，多元线性回归的相关系数分别为r(2)=0.88和S=0.71。
An accurate and generally applicable method for estimating aqueous solubilities for a diverse set of 1297 organic compounds based on multilinear regression and artificial neural network modeling was developed. Molecular connectivity, shape, and atom-type electrotopological state (E-state) indices were used as structural parameters. The data set was divided into a training set of 884 compounds and a randomly chosen test set of 413 compounds. The structural parameters in a 30-12-1 artificial neural network included 24 atom-type E-state indices and six other topological indices, and for the test set, a predictive r(2) = 0.92 and s = 0.60 were achieved. With the same parameters the statistics in the multilinear regression were r(2) = 0.88 and s = 0.71, respectively.