靶向于流感病毒神经氨酸酶新位点的抑制剂设计
CoMFA模型交叉验证系数q2=0.841，其非交叉验证相关系数R2=0.977，标准偏差为0.150，数据组间的平均平方误差与数据组内部的平均平方误差的比值F为176.441。CoMSIA模型的交叉验证系数q2=0.823， R2=0.958，标准偏差为0.199， F值为125.499。本次实验中，神经氨酸酶分为两组，分别是开环受体蛋白和闭环受体蛋白。并用开环受体蛋白，设计了20个新分子，来确定能否有150-空腔。药效团模型中，选取了10个活性较好，结构差异比较大的10个分子进行虚拟筛选，最后结果得出2个有活性的阳性分子。
毕业论文关键词: 3D-QSAR；分子对接；药效团；计算机辅助药物设计
Targeting influenza virus neuraminidase inhibitor design for new site
Abstract：Influenza is caused by a virus of a serious hazard to human health viral respiratory infection, transmitted through the airacutely.In this paper, the method of computer aided drug design, to integrate 3D-QSAR model, molecular docking and pharmacophore group simulation, the result is that we designed a high-efficiency, low toxicity model of statins. Therefore, the 3D-QSAR model is a fast and effective prediction and an essential generic toolsof designingnew drugs. It can provide data for the selection and design of new flu drugs, reducingthe adverse impact ofthe global spread of flu during the day. It provided a theoretical basis and research directions for our greater understanding and designing neuraminidase inhibitors.
CoMFA model cross validation coefficient of q2 =0.841, the non-cross validated coefficient R2=0.977, the standard deviation was 0.150, the ratio between the internal data set of mean square error and data set of mean square error of the F is 176.441. The CoMSIA model of cross validation coefficient q2 =0.823, R2=0.958, the standard deviation was 0.199, F is 125.499. In this experiment, neuraminidase were pided into two groups, namely the open-loop receptor protein and closed-loop receptor protein. With open-loop receptor protein, 20 new molecules were designed to determine whether there are 150-cavity. In pharmacophore model, we selected 10 molecules with different activities and structures for virtual screening, final get two positive molecules.
Keywords:3D-QSAR; molecular docking; pharmacophore; computer aided drug design
目录
第一章 1
1.1研究背景与目的 1
1.2研究现状与进展 2
1.2.1新型神经氨酸酶抑制剂 3
1.2.2对现有抑制剂进行结构修饰以靶向新的结合位点 3
1.2.3通过基于结构的虚拟筛选技术发现新化学分子 5
1.3研究的基本内容 8
1.3.1数据集的收集 8
1.3.2模型构建的准备工作及分子叠合，建立training(训练集)，test(测试集) 8
1.3.3CoMFA模型的建立 9
1.3.4 CoMSIA模型的建立 10
1.3.5训练集预测活性计算 11
1.3.6测试集预测活性计算及新化合物的设计 11
第二章理论原理及计算方法 12
2.1 SYBYL软件 12
2.2 三文定量构效关系 12