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基于神经网络和归纳逻辑编程的定量构效关系。I. 嘧啶对二氢叶酸还原酶的抑制作用

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

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):405-20. doi: 10.1007/BF00125375.

DOI:10.1007/BF00125375
PMID:7815092
Abstract

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure.

摘要

神经网络和归纳逻辑编程（ILP）已与线性回归进行比较，用于对2,4 - 二氨基 - 5 -（取代苄基）嘧啶抑制大肠杆菌二氢叶酸还原酶（DHFR）的定量构效关系（QSAR）进行建模。并且，在随后的论文[赫斯特，J.D.，金，R.D.和斯特恩伯格，M.J.E.《计算机辅助分子设计杂志》，8（1994）421]中，对2,4 - 二氨基 - 6,6 - 二甲基 - 5 - 苯基 - 二氢三嗪对啮齿动物DHFR的抑制作用进行了研究。交叉验证试验对这些方法的预测能力提供了统计学上严格的评估，训练和测试数据随机选取，且所有方法均使用相同的训练数据开发。对于ILP分析，分子由除汉施参数之外的属性表示。使用属性表示时，神经网络和ILP比线性回归表现更好，但差异在统计学上不显著。ILP分析的主要益处在于制定了将抑制剂活性与其化学结构相关联的可理解规则。

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