中文摘要：

通过对天然氨基酸的457种物化性质参数进行主成分分析后得到SVHEHS描述符，用该描述符分别对血管紧张素转化酶（ACE）抑制二肽、三肽、四肽进行表征，并建立了肽结构与活性的神经网络模型。ACE抑制二肽神经网络模型的相关系数、交叉验证相关系数、均方根误差和外部验证相关系数分别为0．946、0．951、0．249、0．852，三肽模型分别为0．973、0．945、0．135、0．813，四肽模型分别为0．915、0．879、0．250、0．814。由此表明SVHEHS描述符结合神经网络对ACE抑制肽的建模效果及模型预测能力均较理想，在此基础上进一步通过平均影响值（Meanimpactvalue，MIV）法确定了显著影响各类肽活性的结构因素，从而为新的强活性ACE抑制肽的分子设计提供了理论基础。

英文摘要：

A new amino acid structure descriptor, namely SVHEHS, was derived from the principal component analysis of 457 physicochemical properties of natural amino acids. The descriptor was used to characterize the structures of angiotensin converting enzyme （ACE） inhibitory dipeptides, tripeptides and tetrapeptides, and then the mathematic models between the structures and the activities of three panels of peptides were established by artificial neural networks. The correlative coefficient （r2） , the cross-validation correlative coefficient（Q2Loo）, root mean square error（RMSE）, external validation correlative coefficient （ Q2ext ） for the dipeptides model were 0. 946, 0. 951, 0. 249 and 0.852, respectively, for tripeptides model were 0.973, 0.945, 0. 135 and 0.813, respectively, and for tetrapeptides model were 0. 915, 0. 879, 0. 250 and 0. 814, respectively. The result indicated that the models based on SVHEHS descriptor combined with neural networks had good fitting and predictive abilities. Moreover, the key structure factors relevant with peptide activities were studied by the mean impact value method. The results can be helpful for the molecular designs of new ACE inhibitory peptides.