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苯甲酰氨类HDAC2抑制剂的3D-QSAR及虚拟筛选研究 被引量:2

3D-QSAR and virtual screening studies of benzamides HDAC2 inhabitors based on pharmacophore model and docking
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摘要 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 the research, QSAR and pharmacophore screening studies were done based on inhibitors of histone deacetylase 2 by traditional comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and Topomer CoMFA methods. For Topomer CoMFA based on molecular fragment modeling, cross-validation q2 value of 0.594 and r2pr^d value of 0.973 were obtained. For CoMFA and CoMSIA based on activity conformation docking alignment, cross-validation q2 value of 0.634, 0.561,r2pred value of 0.905, 0.68 were obtained respectively. For CoMFA and CoMSIA based on pharmacophore model 011, cross-validation q2 value was 0.588, 0.592, r2pred value was 0.68, 0.859 each. The statistical results suggested that five 3D-QSAR models had good stability and predictive ability. Otherwise, 18 highly reactive and diverse molecules established reliable pharmacophore models. The built pharmacophore model 011 and 016 were used to screen the NCI database. The hit compounds were subjected to molecular docking, and then predicted by PASS. The results showed that 18 compounds had high scores greater than 6, and the compounds can be used as new HDAC2 inhibitors.
出处 《计算机与应用化学》 CAS CSCD 北大核心 2014年第5期587-594,共8页 Computers and Applied Chemistry
基金 北京市属高等学校人才强教深化计划中青年骨干人才项目(PHR20100718)
关键词 药效团 3D-QSAR 虚拟筛选 pharmacophore 3D-QSAR virtual screening
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