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本文应用传统比较分子力场分析法CoMFA,比较分子相似性指数法CoMSIA和Topomer CoMFA方法,对组蛋白去乙酰化酶2(HDAC2)的苯甲酰胺类抑制剂进行了构效关系和基于药效团的筛选研究。基于分子片段建模的Topomer CoMFA的交叉验证系数q~2为0.594,预测相关系数r~2_(pred)为0.973。基于对接活性构象叠合得到的CoMFA,CoMSIA的交叉验证相关系数q~2分别为0.634,0.561,预测相关系数r~2_(pred)分别为0.905,0.68。基于药效团模型011叠合的CoMFA,CoMSIA交叉验证相关系数q~2分别为0.588,0.592,预测相关系数r~2_(pred)分别为0.68,0.859。结果表明这5个3D-QSAR模型均具有良好的稳定性和预测能力。另外,由18个活性较高结构多样的分子建立了可靠的药效团模型。运用药效团模型011和016对NCI数据库进行筛选,将筛选得到的分子与HDAC2蛋白酶进行分子对接,并由PASS进行活性验证,最终得到了18个分子,且对接打分值都大于6,可作为新的HDAC2抑制剂。
In this paper, the traditional comparative molecular force field analysis CoMFA, comparative molecular similarity index method CoMSIA and Topomer CoMFA method, the histone deacetylase 2 (HDAC2) benzamide inhibitors of the structure-activity relationship and based on the efficacy Mission screening study. The cross validation coefficient of Topomer CoMFA based on molecular fragment modeling was 0.594, and the predicted correlation coefficient r ~ 2_ (pred) was 0.973. The cross correlation coefficients of CoMFA and CoMSIA obtained based on the docking of the active conformations were q = 2, 0.634 and 0.561, respectively, and the predicted correlation coefficients r ~ 2_ (pred) were 0.905 and 0.68 respectively. CoMFA and CoMSIA cross-validation Coefficients q ~ 2 based on pharmacophore model 011 were 0.588 and 0.592 respectively, and the correlation coefficients r ~ 2 pred were 0.68 and 0.859, respectively. The results show that these five 3D-QSAR models have good stability and predictability. In addition, a reliable pharmacophore model was established by 18 highly active and diverse molecules. The NCI database was screened by pharmacophore model 011 and 016. The screened molecules were molecular docking with HDAC2 protease and their activity was verified by PASS. Finally, 18 molecules were obtained, and the docking scoring values were all greater than 6, New HDAC2 inhibitor.