极光激酶A和B是重要的抗癌药物设计靶标，二者之间结构相似，但在人体的功能不同、在不同癌症中的过表达程度不尽相同。为更好地治愈不同的癌症，有必要寻找新的高活性高选择性抑制剂，并研究其对极光激酶A和B的选择性作用机理。本项目拟建立全面的极光激酶A和B抑制剂数据库，采用自组织神经网络和支持向量机等算法分别建立抑制剂高活性与低活性分类模型、抑制剂活性定量预测模型。利用这些模型对ZINC、中药数据库等虚拟筛选；评价ADMET性质；进行化合物与极光激酶分子对接和动力学模拟；评价化合物骨架的新颖性；对以上得到的（高预测活性、良好ADMET性质、与极光激酶强相互作用、且有新型骨架）化合物进行活性测试。突破已有分子骨架，找到新型的高活性高选择性的极光激酶抑制剂。通过对抑制剂子结构计算、骨架结构分类、抑制剂与极光激酶相互作用研究，阐明其对极光激酶A和B的选择性作用机理，为基于极光激酶的抗癌药物设计提供新思路。

Aurora-A and -B kinases are important targets for anticancer drug discovery. These two homologous Aurora kinases are very similar in sequences and structures, however, they have different functionalities in organisms, and sometimes they are overexpressed in different types of cancer. In order to cure different types of cancer, it is necessary to find new highly active and selective inhibitors to Aurora-A or -B kinase; it is also important to understand the mechanism why an inhibitor can selectively interact with Aurora-A or -B kinase. In this project, firstly we will establish comprehensive databases for inhibitors of Aurora-A and -B kinases, respectively. Based on them, the classification models for distinguishing high and low active inhibitors and the quantitative models for predicting the bioactivity values of inhibitors will be built using Self-Organizing Map (SOM) and Support Vector Machine (SVM) methods, etc. With these models, the compounds virtual screening calculations will be performed (for finding compounds with high predicted bioactivity values) on the databases such as ZINC, PUBCHEM, MDDR, and Traditional Chinese Medicine Database, etc. For the compounds obtained from the above step, their ADMET properties will be computed. Next, the compounds with high predicted bioactivity values and good ADMET properties, are to be further studied individually by calculating the molecular interaction between each compound and Aurora-A (or-B) kinase using docking and Molecular Dynamics (MD) simulation. Then the novelty evaluations for the scaffolds of compounds will be performed. Finally, the compounds supposed to be potential and selective inhibitors of Aurora-A or -B kinase, will be tested by bioassay experimentally. In all, one aim of this project is to find highly active and selective inhibitors of Aurora-A or -B kinase with novel scaffolds. In addition, the mechanism for the selectivity of inhibitors will be investigated by computing their substructures, classifying them according to their scaffolds, and by computing the detail interactions between each inhibitor and Aurora-A or -B kinase, respectively. The study in this project will be helpful for anticancer drug design on the basis of Aurora-A and -B kinases.