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

J D Hirst; R D King; M J Sternberg

doi:10.1007/bf00125376

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

J D Hirst·R D King·M J Sternberg

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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.

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Animals Computer Simulation Folic Acid Antagonists In Vitro Techniques Linear Models Logic Mice Models, Chemical Neural Networks, Computer Rats Structure-Activity Relationship Triazines Journal Article

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J D Hirst, R D King, M J Sternberg. 1994. Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines.. https://doi.org/10.1007/bf00125376

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