摘要
紫杉醇是从紫杉或红豆杉树中提取的一种天然抗癌原料药,具有独特的抗癌机理。由于紫杉醇的种种限制,开发具有更高抗癌活性的类紫杉醇药物具有广阔的前景。紫杉烷二萜是以紫杉醇为母体,通过对其结构的不断修饰得到的一些二代紫杉醇类化合物。本文选用30个结构多样的紫杉烷二帖类化合物作为数据集,随机选取其中24个作为训练集,其它分子作为检验集,采用多元线性回归法(MLR)及主成分回归分析法(PCA)对每个化合物的195个分子参数进行回归分析,分别建立了定量构效关系的最优预测模型;并用检验集检验了所建模型的预测能力。结果表明,多元线性回归法所建模型与主成分回归法所建模型相对比,发现逐步筛选法为最优建模方法。该方法所建模型统计结果良好(R=0.782,SEE=0.202),应用于检验集时结果也比较令人满意(R=0.764,SEP=0.114),模型表现出较强的可靠性和预测性。模型的建立和主要影响因素的确定有助于指导新型紫杉醇类似物药物的筛选和研发。
The Paclitaxel is a natural anti-cancer drug extracted from yews,having the unique anti-cancer mechanism. Because of many factors,developing anti-cancer drugs with higher activity has broader prospects. The taxoids obtained by continuous modification of the paclitaxel are some second-generation paclitaxel compounds. We built a dataset composed of 30 taxoids with diversiform structures,24 compounds served as training set and the rest as test set. Regressed the 195 molecular indices by multivariate linear regression and principal component regression analysis methods and finally got the best predictable mathematic models of their own. From the analysis of the model, stepwise regression analysis was found to be the optimal regression method compared with other multivariate linear regressions and principal component regression analysis. The model built by this method showed satisfactory statistical results （R = 0. 782,SEE = 0. 202）,whose proper predictability was validated by the independent test set （ R = 0. 764,SEP = 0. 114）. The key descriptors were identified,which are valuable and helpful for further researching and development of new paclitaxel analogues drugs.
出处
《化学通报》
CAS
CSCD
北大核心
2010年第4期342-349,共8页
Chemistry
基金
国家自然科学基金项目(10801025)
大连理工大学青年教师培养基金项目(1000-893231)
大连理工大学博士科研启动基金项目(1000-893361)资助
关键词
紫杉烷二帖
分子参数
多元线性回归分析
主成分回归
Taxoid
Molecular indices
Multivariate linear regression analysis
Principal component regression analysis
