Fragment generation and support vector machines for inducing SARs.
SAR QSAR Environ Res
; 13(5): 509-23, 2002 Jul.
Article
em En
| MEDLINE
| ID: mdl-12442768
ABSTRACT
We present a new approach to the induction of SARs based on the generation of structural fragments and support vector machines (SVMs). It is tailored for bio-chemical databases, where the examples are two-dimensional descriptions of chemical compounds. The fragment generator finds all fragments (i.e. linearly connected atoms) that satisfy user-specified constraints regarding their frequency and generality. In this paper, we are querying for fragments within a minimum and a maximum frequency in the dataset. After fragment generation, we propose to apply SVMs to the problem of inducing SARs from these fragments. We conjecture that the SVMs are particularly useful in this context, as they can deal with a large number of features. Experiments in the domains of carcinogenicity and mutagenicity prediction show that the minimum and the maximum frequency queries for fragments can be answered within a reasonable time, and that the predictive accuracy obtained using these fragments is satisfactory. However, further experiments will have to confirm that this is a viable approach to inducing SARs.
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01-internacional
Base de dados:
MEDLINE
Assunto principal:
Inteligência Artificial
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Bases de Dados Factuais
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Modelos Químicos
Tipo de estudo:
Prognostic_studies
Idioma:
En
Revista:
SAR QSAR Environ Res
Assunto da revista:
SAUDE AMBIENTAL
Ano de publicação:
2002
Tipo de documento:
Article
País de afiliação:
Alemanha