Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of LogP

Journal of Chemical Information and Computer Sciences

Volumn 38, Issue 4, 1998, Pages 586-594

  Duprat, A F ^a   Huynh, T ^a   Dreyfus, G ^a  

^a PSL RESEARCH UNIVERSITY   (France)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOMETRY; FACTUAL DATABASE; NONLINEAR SYSTEM; REGRESSION ANALYSIS; STRUCTURE ACTIVITY RELATION;

ALGORITHMS; BIOMETRY; DATABASES, FACTUAL; NEURAL NETWORKS (COMPUTER); NONLINEAR DYNAMICS; REGRESSION ANALYSIS; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 0032112162     PISSN: 00952338     EISSN: None     Source Type: Journal    
DOI: 10.1021/ci980042v     Document Type: Article

References (32)

1. Bishop, C. Neural networks for pattern recognition: Oxford University Press: New York, 1995.
2. Hornik, K.; Stinchcombe, M.; White, H. Multilayer feedforward networks are universal approximators. Neural Networks 1989, 5, 359-366.
3. Hornik, K.; Stinchcombe, M.; White, H.; Auer, P. Degree of approximation results for feedforward networks approximating unknown mappings and their derivatives. Neural Comp. 1994, 6, 1262-1275.
4. Cense, J. M.; Diawara, B.; Legendre, J. J.; Roullet, G. Neural networks prediction of partition coefficients. Chem. Intel. Lab. Syst. 1994, 23, 301-308.
5. oct) using a combination of semiempirical self-consistent reaction field calculations and neural networks. J. Chem. Inf. Comput. Sci. 1995, 35, 905-911.
6. Press, W. H.; Teukolski, W. T.; Vetterling, W. T.; Flannery, B. P. Numerical receipe in C: the art of scientific computing; Cambridge University Press: Cambridge, 1992.
7. Wessel, M. D.; Jurs, P. C. Prediction of reduced ion mobility constants from structural information using multiple linear regression analysis and computational neural networks. Anal. Chem. 1994, 66, 2480-2487.
8. Breindl, A.; Beck, B.; Clark, T.; Glen, R. C. Prediction of the n-octanol/ water partition coefficient, logP, using a combination of semiempirical MO-calculations and a neural network. J. Mol. Model. 1997, 3, 142-155.
9. Nefati, H.; Cense, J. M.; Legendre, J. J. Prediction of the impact sensitivity by neural network. J. Chem. Inf. Comput. Sci. 1996, 36, 804-810.
10. Stoppiglia, H. Méthodes Statistiques de Sélection de Modèles Neuronaux; Applications Financières et Bancaires. Ph.D. Theses. Université Pierre et Marie Curie: Paris, 1997.
11. Egolf, L. M.; Wessel, M. D.; Jurs, P. C. Prediction of boiling points and critical temperatures of industrially important organic compounds from molecular structure. J. Chem. Inf. Comput. Sci. 1994, 34, 947-956.
12. Mitchell, B. E.; Jurs, P. C. Prediction of autoignition temperatures of organic compounds from molecular structure. J. Chem. Inf. Comput. Sci. 1997, 37, 538-547.
13. Tetko, I. V.; Villa, A. E. P.; Livingstone, D. J. Neural network studies. 2. Variable selection. J. Chem. Inf. Comput. Sci. 1996, 36, 794-803.
14. Sutter, J. M.; Dixon, S. L.; Jurs, P. C. Automated descriptor selection for quantitative structure-activity relationships using generalized simulated annealing. J. Chem. Inf. Comp. Sci. 1995, 35, 77-84.
15. Chen, S.; Billings, S. A.; Luo, W. Orthogonal least square methods and their application to non-linear system identification. Int. J. Cont. 1989, 50, 1873-1896.
16. Leontaritis, I. J.; Billings, S. A. Model selection and validation for nonlinear systems. Int. J. Cont. 1987, 45, 311-341.
17. Klopman, G.; Iroff, L. D. Calculation of partition coefficients by the charge density method. J. Comput. Chem. 1981, 2, 157-160.
18. Bodor, N.; Gabanyi, Z.; Wong, C.-K. A new method for the estimation of partition coefficient. J. Am. Chem. Soc. 1989, 111, 3783-3786.
19. Bodor, N.; Huang, M.-J. An extended version of a novel method for the estimation of partition coefficients. J. Pharm. Sci. 1992, 81, 272-281.
20. Klopman, G.; Namboodiri, K.; Schochet, M. Simple method of computing the partition coefficient. J. Comput. Chem. 1985, 6, 28-38.
21. Bodor, N.; Huang, M.-J.; Harget, A. Neural network studies. 3. Prediction of partition coefficients. J. Mol. Struct. (Theochem.) 1994, 309, 259-266.
22. Bodor, N.; Huang, M.-J. A new method for the estimation of the aqueous solubility of organic compouds. J. Pharm. Sci. 1992, 89, 954-960.
23. Insight/Discover, Release 95.0; Biosym/MSI: San Diego, CA, 1995.
24. Dewar, J. S.; Zoebisch, E. G.; Healy, E. F.; Stewart, J. J. P. AM1: a new general purpose quantum mechanical molecular model. J. Am. Chem. Soc. 1985, 107, 3902-3909.
25. Cambridge Crystallographic Database, Version 5.12; Cambridge Crystallographic Data Center; 12 Union Road, Cambridge, CB2 1EZ, U.K, 1995.
26. Leo, A. Calculating log Poet from structures. Chem. Rev. 1993, 93, 1281-1306.
27. Hansch, C.; Leo, A. Exploring QSAR, Fundamentals and applications in chemistry and biology; American Chemical Society; Washington, DC, 1995; Vol. 1.
28. Hansch, C.; Leo, A.; Hoekman, D. Exploring QSAR, Hydrophobic, Electronic, and steric Constants; American Chemical Society: Washington, DC, 1995; Vol. 2.
29. Klopman, G.; Li, J.-Y.; Wang, S.; Dimayuga, M. Computer automated logP calculations based on an extended group contribution approach. J. Chem. Inf. Comput. Sci. 1994, 34, 752-781.
30. CMC3D: MDL Information Systems Inc.: San Leandro, CA, 1996.
31. Stezowski, J. J. Chemical structure properties of tetracycline derivatives. Molecular structure and conformation of the free base derivatives. J. Am. Chem. Soc. 1976, 98, 6012-6018.
32. Pliska, V.; Testa, B.; van de Waterbeemd, H. Lipophilicity in drug action and toxicology; VCH: Weinheim, 1996; Vol. 4.