精品文档---下载后可任意编辑靶向蛋白激酶 AuroraB 的抗肿瘤药物分子水平筛选模型的建立和抑制剂筛选的开题报告摘要：本讨论旨在建立靶向蛋白激酶 AuroraB 的抗肿瘤药物分子水平筛选模型，并利用该模型筛选出具有潜在抑制剂活性的化合物。首先，我们将通过文献调查和分析，筛选出已知的 AuroraB 抑制剂作为模型验证集，利用分子对接和分子动力学模拟方法对其进行模拟实验，探究其与 AuroraB 的结合模式和稳定性。然后，我们将利用计算机与机器学习算法，构建 AuroraB 基于 3D SAR 的药效预测模型。最后，我们将通过虚拟筛选和实验验证相结合的方法，筛选出具有潜在抑制剂活性的化合物。本讨论对于深化理解 AuroraB 与其抑制剂的相互作用机制，为抑制剂的发现和设计提供了新思路和方法，并为靶向 AuroraB 的抗肿瘤药物讨论提供了重要的实验基础和参考。关键词：靶向蛋白激酶 AuroraB，化合物筛选，抑制剂活性，药效预测模型，分子对接，分子动力学模拟方法Abstract:The aim of this study is to establish a molecular level screening model for anti-tumor drugs targeting protein kinase AuroraB, and to screen compounds with potential inhibitor activity using the model.Firstly, we will screen known AuroraB inhibitors as the model validation set through literature investigation and analysis, and simulate their binding mode and stability with AuroraB using molecular docking and molecular dynamics simulation methods. Then, we will use computer and machine learning algorithms to construct AuroraB-based 3D SAR pharmacological prediction model. Finally, we will screen compounds with potential inhibitor activity through virtual screening and experimental verification.This study provides a new idea and method for understanding the interaction mechanism between AuroraB and its inhibitors, and provides an important experimental basis and reference for the development and design of AuroraB inhibitors for anti-tumor drug research.精品文档---下载后可任意编辑keywords: Protein kinase AuroraB, compound screening, inhibitor activity, pharmacological prediction model, molecular docking, molecular dynamics simulation method
