高等学校化学学报

高等学校化学学报 ›› 2000, Vol. 21 ›› Issue (10): 1473.

• 论文 • 上一篇    下一篇

辨识药物定量构效关系的模糊神经网络方法研究

刘平, 程翼宇   

  1. 浙江大学化学工程与生物工程学系, 杭州 310027
  • 收稿日期:1999-12-02 出版日期:2000-10-24 发布日期:2000-10-24
  • 通讯作者: 程翼宇(1958年出生),男,博士,教授,主要从事化学信息学及药物学研究.
  • 基金资助:

    国家自然科学基金(批准号:39870940);国家重点基础研究发展规划项目(批准号:G1999054405);国家攀登计划(批准号:95预

Studies on Quantitative Structure-activity Relationships of Benzodiazepines Using Fuzzy Neural Networks

LIU Ping, CHENG Yi-Yu   

  1. Dept. of Chem. Eng. & Bioeng., Zhejiang University, Hangzhou 310027, China
  • Received:1999-12-02 Online:2000-10-24 Published:2000-10-24

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被引次数

18

摘要： 提出一种基于遗传算法的新型模糊神经网络方法,用于计算Benzodiazepines(BZs)类药物的定量构效关系.这类模糊神经网络综合了神经网络、遗传算法与模糊逻辑的各自优势,具有优良的定量构效关系辨识能力,其学习速度较快,不易陷入局部最小区域;网络知识以模糊语言变量的形式加以表达,不仅易于理解,而且能有效地利用已有的专家经验.一旦通过学习获得规律后,不仅能很好地预测化合物的活性,还能对后续的药物分子设计提供有益的理论指导.

关键词: 模糊神经网络, 模糊逻辑, 神经网络, 遗传算法, 定量构效关系

Abstract: In this paper, a new fuzzy neural network based on genetic algorithms is proposed for quantitative structure-activity relationship(QSAR) studies of benzodiazepines. The method based on GA+FL+NNallows supervised learning of fuzzy rules from significant examples and is affected unsusceptibly by the problem of local extremes. The network's knowledge base has a linguistic representation. This makes it easy for pharmaceutical chemists to understand and interpret. It is possible to introduce current knowledge acquired by researchers simply by adding one or more fuzzy rules to the network's knowledge base. Once the fuzzy knowledge base extracted from examples, it can predict the pharmacological activity of compounds at a high precision. The obtained fuzzy rules can also provide useful guidelines for synthesizing new compounds with a high pharmacological activity.

Key words: Fuzzy neural networks(FNN), Fuzzy logic(FL), Neural networks(NN), Genetic algorithms(GA), QSAR

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