(Q61925420)

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Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms

scientific article published in October 1997

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Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms (English)

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author name string

Frank R. Burden

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1

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Brendan S. Rosewarne

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David A. Winkler

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publication date

October 1997

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published in

Chemometrics and Intelligent Laboratory Systems

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volume

38

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page(s)

127-137

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issue

2

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Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors

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Application of neural networks: quantitative structure-activity relationships of the derivatives of 2,4-diamino-5-(substituted-benzyl)pyrimidines as DHFR inhibitors

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Genetic algorithms: principles of natural selection applied to computation.

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Correlation analysis. Its application to the structure-activity relation of triazines inhibiting dihydrofolate reductase

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A new procedure for improving the predictiveness of CoMFA models and its application to a set of dihydrofolate reductase inhibitors

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

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10.1016/S0169-7439(97)00052-X

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(Q61925420)

Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms

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