Lei Beilei, Xi Lili, Li Jiazhong, Liu Huanxiang, Yao Xiaojun
Department of Chemistry, Lanzhou University, Lanzhou 730000, China.
Anal Chim Acta. 2009 Jun 30;644(1-2):17-24. doi: 10.1016/j.aca.2009.04.019. Epub 2009 Apr 19.
Quantitative structure-activity relationship (QSAR) studies on a series of selective inhibitors of the cyclin-dependent kinase 4 (CDK4) were performed by using two conventional global modeling methods (multiple linear regression (MLR) and support vector machine (SVM)), local lazy regression (LLR) as well as three consensus models. It is remarkable that the LLR model could improve the performance of the QSAR model significantly. In addition, due to the fact that each model can predict certain compounds more accurately than other models, the above three derived models were all used as submodels to build consensus models using three different strategies: average consensus model (ACM), simple weighted consensus model (SWCM) and hat weighted consensus model (HWCM). Through the analysis of the results, the HWCM consensus strategy, firstly proposed in this work, proved to be more reliable and robust than the best single LLR model, ACM and SWCM models.
通过使用两种传统的全局建模方法（多元线性回归（MLR）和支持向量机（SVM））、局部惰性回归（LLR）以及三种共识模型，对一系列细胞周期蛋白依赖性激酶4（CDK4）的选择性抑制剂进行了定量构效关系（QSAR）研究。值得注意的是，LLR模型可以显著提高QSAR模型的性能。此外，由于每个模型对某些化合物的预测比其他模型更准确，上述三个衍生模型都被用作子模型，采用三种不同策略构建共识模型：平均共识模型（ACM）、简单加权共识模型（SWCM）和帽子加权共识模型（HWCM）。通过对结果的分析，本工作首次提出的HWCM共识策略被证明比最佳的单一LLR模型、ACM和SWCM模型更可靠、更稳健。
