Application of cascade correlation networks for structures to chemistry

被引：63

作者：

Bianucci, AM

Micheli, A

Sperduti, A

Starita, A

机构：

[1] Dipartimento Sci Farmaceut, I-56126 Pisa, Italy

[2] Univ Pisa, Dipartimento Informat, I-56125 Pisa, Italy

来源：

APPLIED INTELLIGENCE | 2000年 / 12卷 / 1-2期

关键词：

Cascade Correlation networks; constructive algorithms; gradient descent; QSPR; QSAR;

D O I：

10.1023/A:1008368105614

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 [模式识别与智能系统]; 0812 [计算机科学与技术]; 0835 [软件工程]; 1405 [智能科学与技术];

摘要：

We present the application of Cascade Correlation for structures to QSPR (quantitative structure-property relationships) and QSAR (quantitative structure-activity relationships) analysis. Cascade Correlation for structures is a neural network model recently proposed for the processing of structured data. This allows the direct treatment of chemical compounds as labeled trees, which constitutes a novel approach to QSPR/QSAR. We report the results obtained for QSPR on Alkanes (predicting the boiling point) and QSAR of a class of Benzodiazepines. Our approach compares favorably versus the traditional QSAR treatment based on equations and it is competitive with 'ad hoc' MLPs for the QSPR problem.

引用

页码：117 / 146

页数：30

共 36 条

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