神经药理学报 ›› 2024, Vol. 14 ›› Issue (5): 10-.DOI: 10.3969/j.issn.2095-1396.2024.05.002

• 研究论文 • 上一篇    下一篇

喹唑啉类FGFR4 抑制剂的三维定量构效关系研究

路晨轩,陈茁茁,李国峰,刘星媛,孟宏,覃语涵,李亚鑫   

  1. 河北北方学院药学院,张家口,075000,中国
  • 出版日期:2024-10-25 发布日期:2024-10-26
  • 通讯作者: 李亚鑫,工学博士;研究方向:药物设计;E-mail:lyxhbnu@163.com
  • 作者简介:路晨轩,药学专业本科在读
  • 基金资助:
    国家级大学生创新训练计划项目(No.202410092003);河北省省级大学生创新训练计划项目(No.S202410092021)

Three-Dimensional Quantitative Structure-Activity Relationship Study of Quinazoline-Based FGFR4 Inhibitors

LU Chen-xuan, CHEN Zhuo-zhuo, LI Guo-feng, LIU Xing-yuan, MENG Hong, QIN Yu-han, LI Ya-xin   

  1. Department of Pharmacy, Hebei North University, Zhangjiakou, 075000, China
  • Online:2024-10-25 Published:2024-10-26

摘要:

目的:成纤维细胞生长因子19(fibroblast growth factor 19,FGF19)与成纤维细胞生长因子受体4 (fibroblast growth factor receptor 4,FGFR4)信号通路的异常激活促进了肝细胞癌的增殖,FGFR4 成为了治疗 肝细胞癌的重要靶点之一。该文旨在采用三维定量构效关系的方法阐明喹唑啉类FGFR4 抑制剂结构与活性 的关系。方法:将36 个化合物通过随机抽取的方法分为训练集和测试集,基于比较分子场分析法(comparative molecular field analysis,CoMFA)和比较分子相似指数分析法(comparative molecular similarity indices analysis, CoMSIA)构建三维定量构效关系模型。结果:所建立的CoMFA 和CoMSIA 模型的交叉验证系数(q2)分别为 0.735、0.680,拟合验证系数(r2)分别为0.974、0.981,两个模型预测值与实验值基本一致,说明模型具有预测能力 和良好的稳健性。结论:等势图描绘了不同场效应对化合物活性的影响,为新型喹唑啉类FGFR4 抑制剂的设计 提供了思路。

关键词: FGFR4 抑制剂, 三维定量构效关系, CoMFA, CoMSIA

Abstract:

Objective: The abnormal activation of the Fibroblast Growth Factor 19(FGF19) and Fibroblast Growth Factor Receptor 4 (FGFR4) signaling pathway promotes the proliferation of hepatocellular carcinoma cells, making FGFR4 one of the key targets for treating hepatocellular carcinoma. This study aims to elucidate the relationship between the structure and activity of quinazoline-based FGFR4 inhibitors using a three-dimensional quantitative structureactivity relationship (3D-QSAR) approach. Methods: 36 compounds were randomly divided into a training set and a test set. 3D-QSAR models were constructed using Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA). Results: The cross-validation coefficients (q²) of the CoMFA and CoMSIA models were 0.735 and 0.680, respectively, and the fitting validation coefficients (r²) were 0.974 and 0.981, respectively. The predicted values of the two models were generally consistent with the experimental values,indicating that the models have predictive ability and good robustness. Conclusion: The contour maps illustrate the effects of different field effects on compound activity, providing insights for the design of novel quinazoline-based FGFR4 inhibitors.

Key words: FGFR4 inhibitor, 3D-QSAR, CoMFA, CoMSIA

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