Quantitative structure-activity relationships by neural networks and inductive logic programming. I. The inhibition of dihydrofolate reductase by pyrimidines View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1994-08

AUTHORS

Jonathan D. Hirst, Ross D. King, Michael J. E. Sternberg

ABSTRACT

Neural networks and inductive logic programming (ILP) have been compared to linear regression for modelling the QSAR of the inhibition of E. coli dihydrofolate reductase (DHFR) by 2,4-diamino-5-(substituted benzyl)pyrimidines, and, in the subsequent paper [Hirst, J.D., King, R.D. and Sternberg, M.J.E. J. Comput.-Aided Mol. Design, 8 (1994) 421], the inhibition of rodent DHFR by 2,4-diamino-6,6-dimethyl-5-phenyl-dihydrotriazines. Cross-validation trials provide a statistically rigorous assessment of the predictive capabilities of the methods, with training and testing data selected randomly and all the methods developed using identical training data. For the ILP analysis, molecules are represented by attributes other than Hansch parameters. Neural networks and ILP perform better than linear regression using the attribute representation, but the difference is not statistically significant. The major benefit from the ILP analysis is the formulation of understandable rules relating the activity of the inhibitors to their chemical structure. More... »

PAGES

405-420

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00125375

DOI

http://dx.doi.org/10.1007/bf00125375

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1044077874

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/7815092


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