Cdc20蛋白在多种癌症肿瘤细胞中过量表达，并被认为是癌症治疗的理想靶标。已知的Cdc20抑制剂apcin-A和基于它设计的Cdc20 PROTACs药物相关实验证明，通过抑制或降解Cdc20能有效抑制肿瘤生长。现有的Cdc20抑制剂活性还不够高，达不到临床前应用水平。如何设计安全且活性提高的Cdc20抑制剂是癌症治疗领域的一个重要科学问题。本项目将开发新一代基于成药性模拟构建药效团搜索的虚拟筛选工具，并与基于人工智能的方法集成成为复合型虚拟筛选工具。构建基于片段替换和靶点融合等方法的先导化合物优化方法，构建一套自动化程度高的CADD工具。使用该工具开发新型Cdc20抑制剂，通过分子、细胞和小鼠植瘤抑瘤等实验检验其抗肿瘤活性；实验结果反馈给设计步骤后对其循环优化。新计算机辅助药物设计工具和高活性Cdc20抑制剂药物的开发对药物设计和抗癌研究均有重要意义。

Cdc20 is recognized as an ideal target for cancer therapy since overexpression of the protein was measured in multiple types of tumor tissues. Both known Cdc20 inhibitors (e.g., apcin-A) and Cdc20 PROTACs designed based on apcin-A were experimentally validated in the suppression of tumor growth. However, inhibition activities of these known inhibitors are not high enough for therapy purposes. How to design novel Cdc20 inhibitors with higher activities is an important scientific question in the anticancer research area. The applicant will develop toolkits for druggability molecular simulations, pharmacophore model-based virtual screening, and artificial intelligence based virtual screening methods. These methods will be integrated as a hybrid high-throughput virtual screening tool. Lead optimization approaches (e.g., fragment-based lead optimization) will be implemented and we will build a computer-aided drug design platform. It will facilitate the designing of Cdc20 inhibitors. Molecular-level experiments for testing binding affinities, cell level and small animal (mice) level experiments for measuring suppression of tumor growth will be performed to evaluate the antitumor activities of to be developed Cdc20 inhibitors. Experimental results will be analyzed to improve the virtual screening and optimization cycles. Both the development of computer-aided drug design tools and Cdc20 inhibitors are extremely important to the area of drug discovery and anti-cancer therapies.