Yao X, Zhang X, Zhang R, Liu M, Hu Z, Fan B
Department of Chemistry, Lanzhou University, People's Republic of China.
Comput Chem. 2001 Sep;25(5):475-82. doi: 10.1016/s0097-8485(00)00110-8.
A new method for the prediction of enthalpy of alkanes between C6 and C10 from molecular structures has been proposed. Thirty five calculated descriptors were selected for the description of molecular structures. The first four scores of Principle Component Analysis on the calculated descriptors were used as inputs to predict the enthalpy of alkanes. Models relating relationships between molecular structure descriptors and enthalpy of alkanes were constructed by means of radial basis function neural networks. To get the best prediction results, some strategies were also employed to optimise the learning parameters of the radial basis function neural networks. For the test set, a predictive correlation coefficient of R = 0.9913 and root mean squared error of 0.5876 were obtained.
提出了一种根据分子结构预测C6至C10烷烃焓的新方法。选择了35个计算描述符来描述分子结构。计算描述符的主成分分析的前四个得分用作预测烷烃焓的输入。利用径向基函数神经网络构建了分子结构描述符与烷烃焓之间关系的模型。为了获得最佳预测结果，还采用了一些策略来优化径向基函数神经网络的学习参数。对于测试集，得到的预测相关系数R = 0.9913，均方根误差为0.5876。
