Genetic neural networks for functional approximation

QSAR and Combinatorial Science

Volumn 22, Issue 5, 2003, Pages 519-526

  Chiu, Ting Lan ^a   So, Sung Sau ^a  

^a HOFFMANN LA ROCHE INC   (United States)

Author keywords

Genetic algorithms; Genetic neural networks; Neural networks; QSAR

Indexed keywords

APPROXIMATION ALGORITHMS; COMPUTATIONAL CHEMISTRY; MOLECULAR GRAPHICS; NEURAL NETWORKS;

DESIGN METHOD; FUNCTIONAL APPROXIMATION; FUNCTIONS APPROXIMATIONS; GENETIC NEURAL NETWORK; LEAD OPTIMIZATION; MOLECULAR DESIGN; NEURAL-NETWORKS; PHARMACEUTICAL INDUSTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS ANALYSIS;

GENETIC ALGORITHMS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; DRUG INDUSTRY; MOLECULAR MODEL; PRIORITY JOURNAL; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REACTION OPTIMIZATION; VALIDATION PROCESS;

GOES;

EID: 0042672732     PISSN: 1611020X     EISSN: None     Source Type: Journal    
DOI: 10.1002/qsar.200310004     Document Type: Article

References (44)

1. UNITY. Tripos, Inc., St. Louis, MO.
2. FINGERPRINT. Daylight Chemical Information Systems, Inc., Mission Viejo, CA.
3. Labute, P., A widely applicable set of descriptors, J. Mol. Graph. Model., 18, 464-477 (2000).
4. Wikel, J. H., and Dow, E. R., The use of neural networks for variable selection in QSAR, Bioorg. Med. Chem. Lett., 3, 645-651 (1993).
5. Kirkpatrick, S., Gelatt, C. D., Jr., and Vecchi, M. P., Optimization by simulated annealing, Science, 220, 671-680 (1983).
6. Holland, J. H., Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbor 1975.
7. Wold, S., and Eriksson, L., Statistical validation of QSAR results, in: van de Waterbeemd H. (Ed.), Chemometric Methods in Molecular Design, VCH Publishers, Inc., New York 1995, pp. 309-318.
8. Koza, J. R., Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge 1992.
9. Nicolotti, O., Gillet, V. J., Fleming, P. J., and Green, D. V., Multiobjective optimization in quantitative structure-activity relationships: Deriving accurate and interpretable QSARs, J. Med. Chem., 45, 5069-5080 (2002).
10. Golbraikh, A., and Tropsha, A., Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection., J. Comput.-Aided Mol. Des., 16, 357-369 (2002).
11. Golbraikh, A., and Tropsha, A., Beware of q2!, J. Mol. Graph. Model., 20, 269-276 (2002).
12. So, S.-S., and Karplus, M., Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks: 1. Method and Validations, J. Med. Chem. 40, 4347-4359 (1997).
13. So, S.-S., and Karplus, M., Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks: 2. Applications, J. Med. Chem., 40, 4360-4371 (1997).
14. So, S.-S., and Karplus, M., Evolutionary optimization in quantitative structure-activity relationship: An application of genetic neural networks, J. Med. Chem., 39, 1521-1530 (1996).
15. So, S.-S., and Karplus, M., Genetic neural networks for quantitative structure-activity relationships: improvements and application of benzodiazepine affinity for benzodiazepine/GABAA receptors, J. Med. Chem., 39, 5246-5256 (1996).
16. Yasri, A., and Hartsough, D., Toward an optimal procedure for variable selection and QSAR model building, J. Chem. Inf. Comput. Sci., 41, 1218-1227 (2001).
17. Kauffman, G. W., and Jurs, P. C., Prediction of inhibition of the sodium ion-proton antiporter by benzoylguanidine derivatives from molecular structure, J. Chem. Inf. Comput. Sci., 40, 753-761 (2000).
18. Cartwright, H. M., Applications of artificial intelligence in chemistry, Oxford University Press, Oxford 1993.
19. Mattioni, B. E., and Jurs, P. C., Prediction of glass transition temperatures from monomer and repeat unit structure using computational neural networks, J. Chem. Inf. Comput. Sci., 42, 232-240 (2002).
20. Patankar, S. J., and Jurs, P. C., Prediction of glycine/NMDA receptor antagonist inhibition from molecular structure, J. Chem. Inf. Comput. Sci., 42, 1053-1068 (2002).
21. Mattioni, B. E., and Jurs, P. C., Development of quantitative structure-activity relationship and classification models for a set of carbonic anhydrase inhibitors, J. Chem. Inf. Comput. Sci., 42, 94-102 (2002).
22. Kauffman, G. W., and Jurs, P. C., QSAR and k-nearest neighbor classification analysis of selective cyclooxygenase-2 inhibitors using topologically-based numerical descriptors, J. Chem. Inf. Comput. Sci., 41, 1553-1560 (2001).
23. Patankar, S. J., and Jurs, P. C., Prediction of IC50 values for ACAT inhibitors from molecular structure, J. Chem. Inf. Comput. Sci., 40, 706-723 (2000).
24. Wessel, M. D., Jurs, P. C., Tolan, J. W., and Muskal, S. M., Prediction of human intestinal absorption of drug compounds from molecular structure, J. Chem. Inf. Comput. Sci., 38, 726-735 (1998).
25. McClelland, H. E., and Jurs, P. C., Quantitative structure-property relationships for the prediction of vapor pressures of organic compounds from molecular structures, J. Chem. Inf. Comput. Sci., 40, 967-975 (2000).
26. Møller, M. F., A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, 6, 525-533 (1993).
27. Maddalena, D. J., and Johnston, G. A. R., Prediction of receptor properties and binding affinity of ligands to benzodiazepine/GABAA receptors using artificial neural networks, J. Med. Chem., 38, 715-724 (1995).
28. So, S.-S., van Helden, S. P., van Geerestein, V. J., and Karplus, M., Quantitative structure-activity relationship studies of progesterone receptor binding steroids, J. Chem. Inf. Comput. Sci., 40, 762-772 (2000).
29. Tham, S. Y., and Agatonovic-Kustrin, S., Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives, J. Pharm. Biomed. Anal., 28, 581-590 (2002).
30. Agatonovic-Kustrin, S., Beresford, R., and Yusof, A. P. M., Theoretically-derived molecular descriptors important in human intestinal absorption, J. Pharm. Biomed. Anal., 25, 227-237 (2001).
31. Bakken, G. A., and Jurs, P. C., QSARs for 6-azasteroids as inhibitors of human type 1 5alpha-reductase: Prediction of binding affinity and selectivity relative to 3-BHSD, J. Chem. Inf. Comput. Sci., 41, 1255-1265 (2001).
32. McElroy, N. R., and Jurs, P. C., Prediction of aqueous solubility of heteroatom-containing organic compounds from molecular structure, J. Chem. Inf. Comput. Sci., 41, 1237-1247 (2001).
33. Kauffman, G. W., and Jurs, P. C., Prediction of surface tension, viscosity, and thermal conductivity for common organic solvents using quantitative structure-property relationships, J. Chem. Inf. Comput. Sci., 41, 408-418 (2001).
34. Hodgkin, E. E., and Richards, W. G., Molecular similarity based on electrostatic potential and electric field. Int. J. Quantum Chem. Quantum Biol. Symp., 14, 105-110 (1987).
35. Good, A. C., The calculation of molecular similarity: alternative formulas, data manipulation and graphical display, J. Mol. Graph., 10, 144-151 (1992).
36. Meyer, A. Y., and Richards, W. G., Similarity of molecular shape, J. Comput.-Aided Mol. Des., 5, 427-439 (1991).
37. Borowski, T., Krol, M., Broclawik, E., Baranowski, T. C., Strekowski, L., and Mokrosz, M. J., Application of similarity matrices and genetic neural networks in quantitative structure-activity relationships of 2- or 4-(4-methylpiperazino)pyrimidines: 5-HT(2A) receptor antagonists, J. Med. Chem., 43, 1901-1909 (2000).
38. Dinner, A. R., So, S.-S., and Karplus, M., Use of quantitative structure-property relationships to predict the folding ability of model proteins, Proteins, 33, 177-203 (1998).
39. Dinner, A. R., Verosub, E., and Karplus, M., Use of a quantitative structure-property relationship to design larger model proteins that fold rapidly, Protein Eng., 12, 909-917 (1999).
40. Leardi, R., and González, A. L., Genetic algorithms applied to feature selection in PLS regression: How and when to use them, Chemom. Intell. Lab. Sys., 41, 195-207 (1998).
41. Tetko, I. V., Livingstone, D. J., and Luik, A. I., Neural network studies. 1. Comparison of overfitting and overtraining, J. Chem. Inf. Comput. Sci., 35, 826-833 (1995).
42. Andrea, T. A., and Kalayeh, H., Applications of neural networks in quantitative structure-activity relationships of dihydrofolate reductase inhibitors, J. Med. Chem., 34, 2824-2836 (1991).
43. Schneider, G., and Wrede, P., Optimizing amino acid sequences by simulated molecular evolution, in: Jesshope C., Jossifov V., and Wilhelmi W. (Ed.), Parallel Computing and Cellular Automata, Akademie-Verlag, Berlin 1993, pp. 335-346.
44. Burden, F. R., Rosewarne, B. S., and Winkler, D. A., Predicting maximum bioactivity by effective inversion of neural networks using genetic algorithms, Chemom. Intell. Lab. Sys., 38, 127-137 (1997).