Quantitative structure-activity relationships by neural networks and inductive logic programming. II. The inhibition of dihydrofolate reductase by triazines
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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.
Key wordsQSAR Artificial intelligence Neural networks DHFR inhibitors
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- 1.Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 405.Google Scholar
- 2.Silipo, C. and Hansch, C., J. Am. Chem. Soc., 97 (1975) 6849.Google Scholar
- 3.Andrea, T.A. and Kalayeh, H., J. Med. Chem., 34 (1991) 2824.Google Scholar
- 4.Hansch, C. and Fukunga, J., CHEMTECH, (1977) 120.Google Scholar
- 5.Hansch, C. and Silipo, C., J. Med. Chem., 17 (1974) 661.Google Scholar
- 6.Minitab, release 7.2, VAX/VMS version, Minitab, Inc., Pennsylvania State University, Philadelphia, PA, 1989.Google Scholar
- 7.Muggleton, S. and Feng, C., In Arikawa, S., Goto, S., Ohsuga, S. and Yokomori, T. (Eds.) Proceedings of the First Conference on Algorithmic Learning Theory, Japanese Society of Artifical Intelligence, Ohmsha Press, Tokyo, 1990, pp. 368–381.Google Scholar
- 8.Silipo, C. and Hansch, C., J. Med. Chem., 19 (1976) 62.Google Scholar
- 9.So, S.-S. and Richards, W.G., J. Med. Chem., 35 (1992) 3201.Google Scholar