The discovery of indicator variables for QSAR using inductive logic programming

Journal of Computer-Aided Molecular Design

Volumn 11, Issue 6, 1997, Pages 571-580

  King, Ross D ^a   Srinivasan, Ashwin ^b  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Artificial intelligence; Machine learning; Regression

Indexed keywords

BIOACTIVITY; COMPUTER CIRCUITS; FORECASTING; INDUCTIVE LOGIC PROGRAMMING (ILP); LEARNING SYSTEMS; REGRESSION ANALYSIS;

CENTRAL PROBLEMS; DRUG DESIGN; INDICATOR VARIABLES; INDUCTIVE LOGIC; LOGIC-PROGRAMMING; MACHINE-LEARNING; PROGRAMMING TECHNIQUE; QSAR STUDIES; REGRESSION; REGRESSION EQUATION;

ARTIFICIAL INTELLIGENCE;

ADENOSINE TRIPHOSPHATASE (CALCIUM); DIHYDROFOLATE REDUCTASE; ENZYME INHIBITOR; FOLIC ACID ANTAGONIST; MUTAGENIC AGENT; PYRIMIDINE DERIVATIVE; TRIAZINE DERIVATIVE;

ANIMAL; ARTICLE; BIOLOGICAL MODEL; CHEMISTRY; COMPUTER PROGRAM; COMPUTER SIMULATION; DRUG ANTAGONISM; REGRESSION ANALYSIS; STRUCTURE ACTIVITY RELATION;

ANIMALS; CALCIUM-TRANSPORTING ATPASES; COMPUTER SIMULATION; ENZYME INHIBITORS; FOLIC ACID ANTAGONISTS; MODELS, BIOLOGICAL; MUTAGENS; PYRIMIDINES; REGRESSION ANALYSIS; SOFTWARE; STRUCTURE-ACTIVITY RELATIONSHIP; TETRAHYDROFOLATE DEHYDROGENASE; TRIAZINES;

EID: 0031261477     PISSN: 0920654X     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1007967728701     Document Type: Article

References (40)

1. Hansch, C., Maloney, P.P., Fujita, T. and Muir, R.M., Nature, 194 (1962) 178.
2. Martin, Y.C., Quantitative Drug Design: A Critical Introduction, Marcel Dekker, New York, NY, U.S.A., 1978.
3. Ramsden, C. (Ed.) Comprehensive Medicinal Chemistry, Vol. 4, Pergamon, Oxford, U.K., 1990.
4. Trinajstic, N., Chemical Graph Theory, CRC Press, Boca Raton, FL, U.S.A., 1983.
5. Debnath, A.K., Lopez de Compadre, R.L., Debnath, G., Shusterman, A.J. and Hansch, C., J. Med. Chem., 34 (1991) 786.
6. Klopman, G., J. Am. Chem. Soc., 106 (1984) 7315.
7. Klopman, G., Quant. Struct.-Act. Relatsh., 11 (1992) 176.
8. Hopfinger, A.J., J. Am. Chem. Soc., 102 (1980) 7196.
9. Cramer, R.D., Patterson, D.E. and Bunce, J.D., J. Am. Chem. Soc., 110 (1988) 5959.
10. Hansch, C., Li, R.-I., Blaney, J.M. and Langridge, R., J. Med. Chem., 25 (1982) 777.
11. Muggleton, S. and Feng, C., In Proceedings of the First Conference on Algorithmic Learning Theory, Japanese Society of Artificial Intelligence, Tokyo, Japan, 1990, pp. 368-381.
12. King, R.D., Muggleton, S., Lewis, R.A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 89 (1992) 11322.
13. Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 405.
14. Hirst, J.D., King, R.D. and Sternberg, M.J.E., J. Comput.-Aided Mol. Design, 8 (1994) 421.
15. Muggleton, S.H., New Gen. Comput., 13 (1995) 245.
16. King, R.D., Muggleton, S.H., Srinivasan, A. and Sternberg, M.J.E., Proc. Natl. Acad. Sci. USA, 93 (1996) 438.
17. King, R.D. and Srinivasan, A., Environ. Health Perspect., 104 (Suppl. 5) (1996) 1031.
18. DeLong, H., A Profile of Mathematical Logic, Addison-Wesley, Reading, MA, U.S.A., 1970.
19. Bahler, D. and Bristol, D.W., In Intelligent Systems for Molecular Biology-93, AAI/MIT Press, Menlo Park, CA, U.S.A., 1993.
20. Lee, Y., Buchanan, B.G., Mattison, D.M., Klopman, G. and Rosenkranz, H.S., Mutat. Res., 328 (1995) 127.
21. Silipo, C. and Hansch, C., J. Am. Chem. Soc., 97 (1975) 6849.
22. Davis, A.M., Gensmantel, N.P., Johansson, E. and Marriott, D.P., J. Med. Chem., 37 (1994) 963.
23. Norusis, M.J., SPSS: Base System User Guide, Release 6.0, SPSS Inc., Chicago, IL, U.S.A., 1994.
24. Wallace, C.S. and Freeman, P.R., J. R. Statist. Soc., B49 (1987) 195.
25. Srinivasan, A., Muggleton, S.H., Sternberg, M.J.E. and King, R.D., A.I. Journal, (1997) in press.
26. Topliss, J. and Edwards, R.P., J. Med. Chem., 22 (1979) 1238.
27. Wold, S., Technometrics, 20 (1978) 397.
28. Champness, J.N., Stammers, D.K. and Beddell, C.R., FEBS Lett., 199 (1986) 61.
29. Matthews, D.A., Bolin, J.T., Burridge, J.M., Filman, D.J., Volz, K.W., Kaufman, B.T., Beddell, C.R., Champness, J.N., Stammers, D.K. and Kraut, J., J. Biol. Chem., 260 (1985) 381.
30. Roth, B., Aig, E., Rauckman, B.S., Srelitz, J.Z., Phillips, A.P., Ferone, R., Bushby, S.R.M. and Siegel, C.W., J. Med. Chem., 24 (1981) 933.
31. Andrea, T.A. and Kalayeh, H., J. Med. Chem., 34 (1991) 2824.
32. So, S.-S. and Richards, W.G., J. Med. Chem., 35 (1992) 3201.
33. Kubinyi, H., Quant. Struct.-Act. Relatsh., 13 (1994) 285.
34. Glen, R.A. and Payne, A.W.R., J. Comput.-Aided Mol. Design, 9 (1995) 181.
35. Michie, D., Spiegelhalter, D.J. and Taylor, C.C., Machine Learning, Neural and Statistical Classification, Ellis Horwood, London, U.K., 1994.
36. Frank, I.E. and Friedman, J.H., Technometrics, 35 (1993) 109.
37. Michalski, R.S., In Michalski, R.S., Carbonnel, J. and Mitchell, T. (Eds.) Machine Learning: An Artificial Approach, Morgan Kaufmann, Los Altos, CA, U.S.A., 1986, pp. 83-134.
38. Lavrac, N. and Dzeroski, S., Inductive Logic Programming Techniques and Applications, Ellis Horwood, London, U.K., 1994.
39. King, R.D., Srinivasan, A. and Sternberg, M.J.E., New Gen. Comput., 13 (1995) 411.
40. Muggleton, S., Page, D. and Srinivasan, A., In Inductive Logic Programming 96, Stockhohn, Sweden, 1996.