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利用神经网络和归纳逻辑编程的定量构效关系。II. 三嗪对二氢叶酸还原酶的抑制作用

Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.

作者信息

Hirst J D, King R D, Sternberg M J

机构信息

Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, London, U.K.

出版信息

J Comput Aided Mol Des. 1994 Aug;8(4):421-32. doi: 10.1007/BF00125376.

DOI:10.1007/BF00125376
PMID:7815093
Abstract

One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR)--the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives--has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.

摘要

用于建立定量构效关系（QSAR）的最大可用数据集之一——2,4-二氨基-6,6-二甲基-5-苯基二氢三嗪衍生物对二氢叶酸还原酶（DHFR）的抑制作用——已用于神经网络、归纳逻辑编程（ILP）和线性回归的六重交叉验证试验。这些方法的预测能力之间未发现统计学上的显著差异。然而，属性对分子的表示是ILP方法不可或缺的一部分，它提供了关于药物-受体相互作用的可理解规则。

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本文引用的文献

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The discovery of indicator variables for QSAR using inductive logic programming.使用归纳逻辑编程发现用于定量构效关系的指示变量。
J Comput Aided Mol Des. 1997 Nov;11(6):571-80. doi: 10.1023/a:1007967728701.
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