基于启发式方法和支持向量机方法预测有机物的热导率

构建147个有机物分子结构与其热导率值之间的定量结构-性质关系(QSPR)模型, 探讨影响有机物热导率的结构因素. 以147个化合物作为样本集, 随机选择118个作为训练集, 29个作为测试集. 应用CODESSA软件计算了组成、拓扑、几何、静电和量子化学等描述符, 通过启发式方法(HM)筛选得到5个结构参数并建立线性回归模型; 用所选5个结构参数作为支持向量机(SVM)的输入, 建立非线性的支持向量机回归模型. 预测结果表明: 支持向量机回归模型的性能(复相关系数R²=0.9240)虽略低于启发式回归模型的性能(R²=0.9267), 但是支持向量机方法预测性能(R²=0.9682)高于启发式方法的预测性能(R²=0.9574), 对于QSPR模型来说, 预测性能更重要. 因此, 总体来说支持向量机方法优于启发式方法. 支持向量机方法和启发式方法的提出为工程上提供了一种根据分子结构预测有机物热导率的新方法.     

A new approach for the tissue-blood partition coefficients of neutral and ionized compounds

A nonlinear model equation based on tissue composition (a content of lipids, proteins, and water) for the tissue-blood partition coefficients of compounds was further developed in this paper to account for the neutral and ionized forms of the compounds. The nonlinear model equation may be considered as the expressive form of the Hansch equation in nonlinear spaces (or multiphase system). Based on this model our nonlinear regression analysis for neutral and ionized compounds partitioning into kidney, brain, muscle, lung, liver, heart, and fat resulted in equations with high fitting power (training set: n=201, r=0.905, s=0.291, Q=0.890) and strong predictive power (test set: n=64, r=0.906, s=0.247). These results showed that the equilibrium distribution of a compound in a variety of tissues is essentially the equilibrium distribution of the compound in tissue (chemical) compositions. Neutral and ionic forms of a compound as well as in different tissue (chemical) compositions have a different mechanism of action in vivo.