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化学学报
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化学学报 ›› 2007, Vol. 65 ›› Issue (15): 1420-1426. 上一篇    下一篇

研究论文

人类A3腺苷受体拮抗剂1,2,4-三唑并[1,5-α]喹喔啉衍生物的QSAR研究

战金辉, 李吉来, 黄旭日*, 左明辉, 孙家锺   

  1. (吉林大学理论化学研究所理论化学计算国家重点实验室 长春 130023)
  • 投稿日期:2006-10-30 修回日期:2006-12-25 发布日期:2007-08-14
  • 通讯作者: 黄旭日

QSAR Study on 1,2,4-Triazolo[1,5-α]quinoxaline Derivatives as Human A3 Adenosine Receptor Antagonists

ZHAN Jin-Hui; LI Ji-Lai; HUANG Xu-Ri*; ZUO Ming-Hui; SUN Chia-Chung   

  1. (Institute of Theoretical Chemistry, State Key Laboratory of Theoretical and Computational Chemistry, Jilin University, Changchun 130023)
  • Received:2006-10-30 Revised:2006-12-25 Published:2007-08-14

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采用遗传算法构建了27种人类腺苷受体拮抗剂1,2,4-三唑并[1,5-α]喹喔啉衍生物与受体之间的亲和性的QSAR模型. 为得到理想模型, 计算了拓扑学、热力学、空间、电子拓扑状态和量子化学描述符. 结合这些参数得到最终模型: pKi＝13.407－0.027*FC－8E－0.033*FC－8N＋0.845*Atype_C_28－19.493*Shadow_XYfrac.计算得到的统计学指标为: LOF＝0.291, r2＝0.766, radj2＝0.723, F-test＝17.974, PRESS＝3.469, CV-r2＝0.791. 通过对模型进行分析, 得到如下结论: 降低C-8位亲电、亲核原子的前线电子密度的权重和分子在XY平面的投影分数, 增加疏水性原子类型描述符Atpye_C_28的值, 都对增加化合物分子与受体的亲和性有利. 利用此模型合理的设计了两个新的化合物, 并预测具有较高的结合活性. 该研究为喹喔啉衍生物作为人类A3腺苷受体拮抗剂的结构改造提供理论指导, 并为进一步研究受体与配体亲和性机理奠定理论基础.

关键词: 遗传算法, 亲和性, 量子化学描述符, QSAR

Binding affinity data of twenty seven 1,2,4-triazolo[1,5-α]quinoxaline derivatives for human adenosine A3 receptor subtype have been subjected to QSAR analysis using genetic algorithm (GA) method. In order to obtain perfect statistical quality model, descriptors from topological, thermodynamic, spatial, electrotopological and quantum chemical class were calculated. The best model is shown below and its index in statistially is LOF＝0.291, r2＝0.766, radj2＝0.723, F-test＝17.974, PRESS＝3.469, CV-r2＝0.791; pKi＝13.407－0.027*FC－8E－0.033*FC－8N＋0.845*Atype_C_28－19.493*Shadow_XYfrac. Some conclusions are presented that the binding affinity of these compounds increases with reducing weighted electrophilic and nucleophilic atomic frontier electron density at the C-8 position, the fraction of area of molecular shadow in the XY plane over area of enclosing rectangle, and increasing the hydrophobic atom type descriptor (Atpye_C_28). According to this model, we also design two new compounds and predict that they may have high binding affinities. We expect that the present study provide theoretical instruction on the structural modification of quinoxaline derivatives as human A3 adeno-sine receptor antagonists, and may be helpful for probing the mechanism of action between the receptor and its antagonists.

Key words: genetic algorithm, binding affinity, quantum chemical descriptor, QSAR