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Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines
Authors:Jonathan D Hirst  Ross D King  Michael J E Sternberg
Institution:(1) Biomolecular Modelling Laboratory, Imperial Cancer Research Fund, 44 Lincoln's Inn Fields, P.O. Box 123, WC2A 3PX London, U.K.;(2) Present address: Department of Chemistry, Mellon Institute, 4400 Fifth Avenue, Box 77, 15213 Pittsburgh, PA, U.S.A.
Abstract:Summary One of the largest available data sets for developing a quantitative structure-activity relationship (QSAR) — the inhibition of dihydrofolate reductase (DHFR) by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazine derivatives — has been used for a sixfold cross-validation trial of neural networks, inductive logic programming (ILP) and linear regression. No statistically significant difference was found between the predictive capabilities of the methods. However, the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
Keywords:QSAR  Artificial intelligence  Neural networks  DHFR inhibitors
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