Quantitative structure-activity relationships of Benzodiazepines by recursive cascade correlation

Author

Bianucci, Anna Maria ; Micheli, Alessio ; Sperduti, Alessandro ; Starita, Antonina

Author_Institution

Dipt. di Sci. Farmaceutiche, Pisa Univ., Italy

Volume

1

fYear

1998

fDate

4-8 May 1998

Firstpage

117

Abstract

An application of recursive cascade correlation to the quantitative structure-activity relationships (QSAR) of a class of Beneodiazepines is presented. Recursive cascade correlation is a neural network model recently proposed for the processing of structured data. This allows the direct treatment of the chemical compounds as labeled ordered trees, which constitutes a novel approach to QSAR. Our approach compares favorably versus the traditional QSAR treatment based on equations

Keywords

chemistry computing; correlation methods; learning (artificial intelligence); medical computing; neural nets; pattern classification; trees (mathematics); Beneodiazepines; acyclic graphs; biological response; chemical compounds; drug; labelled directed trees; learning; molecular structure representation; pattern classification; quantitative structure-activity relationships; recursive cascade correlation; recursive neural networks; Biological information theory; Biological system modeling; Chemical compounds; Differential equations; Drugs; Feedforward neural networks; Helium; Neural networks; Relays; Systematics;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7576

Print_ISBN

0-7803-4859-1

Type

conf

DOI

10.1109/IJCNN.1998.682247

Filename

682247