摘要
用分子力学、分子模拟退火动力学、量子化学等方法对转化生长因子-β受体激酶(TβR-Ⅰ)的取代吡唑类抑制剂进行了结构优化.用遗传算法(GFA)并结合多元线性回归技术(MLR),对该类抑制剂进行了定量构效关系研究,筛选出了影响抑制剂活性的主要因素,建立了定量构效关系方程(QSAR).结果表明,抑制剂分子的最高占据轨道能量EHOMOJ、urs的面积加权部分负电荷表面积WNSA1等是影响AHSPs抑制活性的主要分子参数.所得模型对该类化合物关于TβR-Ⅰ的抑制活性有良好的评估和预测效果(R=0.977,R2cv=0.864,s=0.178,F=51.93).
The geometry structure of pyrazole-based inhibitors of the transformation growth factor-β type I receptor kinase domain （TβR-Ⅰ ） was optimized through the methods of quantum chemistry, molecular mechanism and molecular anneal dynamics. The quantitative structure-activity relationship of these inhibitors, in regard to TβR-Ⅰ , was systematically studied using the genetic function approximation （GFA） and multiple linear regression （MLR）. Some main independent factors affecting the activity of the compounds were selected out, and then the QSAR equation was established. It has found that scriptor WNSA1 - surface-weighted partial negative the energy of the highest occupied orbital （HOMO）, Jurs desurface area etc., are the main independent factors contributing to the inhibiting activity of the compounds. The fitting correlation coefficient R, the cross-validation Rcv2 the standard error of the regression model and F test value for the model established by this study are 0. 977,0. 864, 0. 178, and 51.93, respectively. The results suggest that this model has good predictability.
出处
《湘南学院学报》
2006年第5期45-48,共4页
Journal of Xiangnan University
基金
2006年湖南省教育厅自然科学基金资助
关键词
转化生长因子-β受体激酶(TβR-Ⅰ)
定量构效关系(QSAR)
模拟退火
量子化学
遗传算法
the transformation growth factor-β type Ⅰ receptor kinase domain （TβR-Ⅰ ）
The quantitative structure-activity relationship（QSAR）
anneal
quantum chemistry
Genetic function approximation（GFA）
