基于极光激酶的计算机辅助药物设计的研究
发布时间：2018-09-08 09:31
【摘要】：极光激酶A是一种广为认可的抗癌药物设计的靶标,其抑制剂的设计研发有重要的研究意义。利用计算机辅助药物设计的各种方法,通过对已知的极光激酶A抑制剂进行构效关系研究,包括建立分类模型和定量预测模型,以及基于小分子配体化合物的虚拟筛选,可以从小分子化合物数据库中筛选出具有抑制极光激酶A的苗头化合物。本课题研究主要包含为以下三方面的研究内容： (1)利用自组织神经网络和支持向量机法建立了极光激酶A抑制剂生物活性的分类模型。对已知结构和活性的1463个极光激酶A抑制剂,基于生物活性为阈值,选择了29个ADRIANA.Code结构特征符,用自组织神经网络和支持向量机法分别建立分类模型ModelA1和ModelA2。其中ModelA1对训练集和测试集的预测正确率分别为91.19%和86.10%,ModelA2对训练集和测试集的预测正确率分别为94.13%和86.11%。此外还利用ECFP4指纹图谱分析抑制剂生物活性与其子结构的关系。 (2)利用多元线性回归和支持向量机建立了极光激酶A抑制剂生物活性的定量预测模型研究。根据抑制剂的三种不同酶学活性测定方式,将抑制剂分成三个子集,分别包括356/302/279个化合物。每个子集用随机和自组织神经网络两种方法划分训练集和测试集,再分别用多元线性回归法和支持向量机建立模型。并对每个子集建立了四个定量预测模型。所有模型对测试集的活性值定量预测相关系数R均不小于0.77。模型均通过随机分布法检验,显示出良好的预测能力。 (3)基于极光激酶A抑制剂为配体的虚拟筛选研究。依据分子三维形状相似性和静电相似性,主要利用ROCs和EON软件,探寻建立不同目标查询式(query)的方法,最终选定化合物vx680为query,进行虚拟筛选,从近五百万个小分子数据库中得到500个与目标查询式最相似的化合物。这些化合物通过最优分类模型和最优定量预测模型逐一筛选后,得到预测活性值小于10nM的23个化合物。其被认为是具有抑制极光激酶A的苗头化合物。 本研究通过对极光激酶A抑制剂生物活性的分类和定量预测研究,建立了一系列的定性和定量预测模型。通过对极光激酶A抑制剂的三维分子形状和静电相似的虚拟筛选研究,找到了一些潜在的抑制极光激酶A的苗头化合物。
[Abstract]:Aurora kinase A is a widely recognized target for the design of anticancer drugs, and the design and development of its inhibitors is of great significance. By using various methods of computer-aided drug design, the structure-activity relationships of known Aurora inhibitors were studied, including the establishment of classification models and quantitative prediction models, and virtual screening based on small molecular ligands. Seedling compounds with inhibition of aurora kinase A can be screened from a small molecular compound database. The main contents of this paper are as follows: (1) the classification model of the biological activity of Aurora kinase A inhibitors is established by using self-organizing neural network and support vector machine. For 1463 Aurora kinase A inhibitors with known structure and activity, based on the threshold of biological activity, 29 ADRIANA.Code structural characteristics were selected. The classification model ModelA1 and ModelA2. were established by using self-organizing neural network and support vector machine method, respectively. The prediction accuracy rate of ModelA1 for training set and test set is 91.19% and 86.10% respectively for training set and test set are 94.13% and 86.11% respectively. In addition, the relationship between the bioactivity of inhibitors and their substructures was analyzed by ECFP4 fingerprinting. (2) A quantitative prediction model for the bioactivity of Aurora kinase A inhibitors was established by using multiple linear regression and support vector machine. The inhibitors were divided into three subsets according to three different enzymatic activities of inhibitors, consisting of 356 / 302 / 279 compounds. Each subset is divided into training set and test set by stochastic and self-organizing neural network methods, and then the model is established by multivariate linear regression method and support vector machine, respectively. Four quantitative prediction models are established for each subset. The correlation coefficient R of all models for quantitative prediction of activity values of test sets is not less than 0.77. The models were tested by random distribution method and showed good predictive ability. (3) Virtual screening study based on Aurora kinase A inhibitor as ligand. According to the similarity of three-dimensional and electrostatic shapes of molecules, the methods of establishing different target query (query) were explored by using ROCs and EON software. Finally, the compound vx680 was selected for virtual screening of query,. 500 compounds most similar to the target query were obtained from nearly five million small molecular databases. These compounds were screened one by the optimal classification model and the optimal quantitative prediction model, and 23 compounds whose predictive activity values were less than 10nM were obtained. It is thought to be a seeding compound that inhibits auroral kinase A. In this study, a series of qualitative and quantitative prediction models were established based on the classification and quantitative prediction of the biological activity of Aurora kinase A inhibitors. Based on the virtual screening of the three-dimensional molecular shape and electrostatic similarity of Aurora kinase A inhibitors, some potential antagonist compounds of aurora kinase A were found.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R91-39
[Abstract]:Aurora kinase A is a widely recognized target for the design of anticancer drugs, and the design and development of its inhibitors is of great significance. By using various methods of computer-aided drug design, the structure-activity relationships of known Aurora inhibitors were studied, including the establishment of classification models and quantitative prediction models, and virtual screening based on small molecular ligands. Seedling compounds with inhibition of aurora kinase A can be screened from a small molecular compound database. The main contents of this paper are as follows: (1) the classification model of the biological activity of Aurora kinase A inhibitors is established by using self-organizing neural network and support vector machine. For 1463 Aurora kinase A inhibitors with known structure and activity, based on the threshold of biological activity, 29 ADRIANA.Code structural characteristics were selected. The classification model ModelA1 and ModelA2. were established by using self-organizing neural network and support vector machine method, respectively. The prediction accuracy rate of ModelA1 for training set and test set is 91.19% and 86.10% respectively for training set and test set are 94.13% and 86.11% respectively. In addition, the relationship between the bioactivity of inhibitors and their substructures was analyzed by ECFP4 fingerprinting. (2) A quantitative prediction model for the bioactivity of Aurora kinase A inhibitors was established by using multiple linear regression and support vector machine. The inhibitors were divided into three subsets according to three different enzymatic activities of inhibitors, consisting of 356 / 302 / 279 compounds. Each subset is divided into training set and test set by stochastic and self-organizing neural network methods, and then the model is established by multivariate linear regression method and support vector machine, respectively. Four quantitative prediction models are established for each subset. The correlation coefficient R of all models for quantitative prediction of activity values of test sets is not less than 0.77. The models were tested by random distribution method and showed good predictive ability. (3) Virtual screening study based on Aurora kinase A inhibitor as ligand. According to the similarity of three-dimensional and electrostatic shapes of molecules, the methods of establishing different target query (query) were explored by using ROCs and EON software. Finally, the compound vx680 was selected for virtual screening of query,. 500 compounds most similar to the target query were obtained from nearly five million small molecular databases. These compounds were screened one by the optimal classification model and the optimal quantitative prediction model, and 23 compounds whose predictive activity values were less than 10nM were obtained. It is thought to be a seeding compound that inhibits auroral kinase A. In this study, a series of qualitative and quantitative prediction models were established based on the classification and quantitative prediction of the biological activity of Aurora kinase A inhibitors. Based on the virtual screening of the three-dimensional molecular shape and electrostatic similarity of Aurora kinase A inhibitors, some potential antagonist compounds of aurora kinase A were found.
【学位授予单位】：北京化工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：R91-39
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