Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation
Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation
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DOI：
10.1021/ci025590u
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发表时间：
2003-03
期刊：
影响因子：
--
通讯作者：
A. Yan;J. Gasteiger
中科院分区：
文献类型：
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作者：
A. Yan;J. Gasteiger
利用多元线性回归(MLR)分析和反向传播(BPG)神经网络建立了预测1293种有机化合物水溶解度的两个定量模型。分子由代表3D结构的径向分布函数(RDF)代码的一组32个值和8个附加描述符描述。基于Kohonen自组织神经网络映射，1293种化合物被分为797种化合物的训练集和496种化合物的测试集。得到的模型具有很好的预测能力：对于测试集，反向传播神经网络方法的相关系数为0.96，标准差为0.59。
Two quantitative models for the prediction of aqueous solubility of 1293 organic compounds were developed by a Multilinear Regression (MLR) analysis and a Back-Propagation (BPG) neural network. The molecules were described by a set of 32 values of a Radial Distribution Function (RDF) code representing the 3D structure and eight additional descriptors. The 1293 compounds were divided into a training set of 797 compounds and a test set of 496 compounds based on a Kohonen self-organizing neural network map. The obtained models show a good predictive power: for the test set, a correlation coefficient of 0.96 and a standard deviation of 0.59 were achieved by the back-propagation neural network approach.