Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines
Journal of Computer-Aided Molecular DesignPublished 1 August 1994
Jonathan D. Hirst, Ross D. King, Michael J.E. Sternberg
Citations86
SJR quartileQ2
SJR score0.58
SNIP0.73
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TL;DR
No statistically significant difference was found between the predictive capabilities of the methods, but the representation of molecules by attributes, which is integral to the ILP approach, provides understandable rules about drug-receptor interactions.
Abstract
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
ChemistryComputer ScienceBiochemistry, Genetics and Molecular Biology
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