Support vector machine applied in QSAR modelling

Chinese Science Bulletin

Volumn 50, Issue 20, 2005, Pages 2291-2296

  Mei, Hu ^a,b   Zhou, Yuan ^a   Liang, Guizhao ^a   Li, Zhiliang ^a,b  

^a CHONGQING UNIVERSITY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

Back propagation (BP) artificial neural network (ANN); Partial least squares (PLS); Quantitative structure activity relationship (QSAR); Support rector machine (SVM)

Indexed keywords

EID: 33749632782     PISSN: 10016538     EISSN: 18619541     Source Type: Journal    
DOI: 10.1360/982005-30     Document Type: Article

References (17)

1. Cortes, C., Vapnik, V., Support vector networks, Machine Learning, 1995, 20(3): 273-293.
2. Flach, P. A., On the state of the art in machine learning: A personal review, Artificial Intelligence, 2001, 131(1): 199-222.
3. Zhang, X. G., Introduction to statistical learning theory and support vector machine, Acta Automatica Sinica, 2000, 26(1): 32-42.
4. Sánchez, A. V. D., Advanced support vector machines and kernel methods, Neurocomputing, 2003, 55(1): 5-20.
5. Chaucharda, F., Cogdillb, R., Rousselc, S. et al., Application of LS-SVM to non-linear phenomena in NIR spectroscopy: development of a robust and portable sensor for acidity prediction in grapes, Chemom. Intell. Lab. Syst., 2004, 71(2): 141-150.
6. Hua, S., Sun, Z., A novel method of protein secondary structure prediction with high segment overlap measure:support vector machine approach, J. Mol. Biol., 2001, 308(2): 397-407.
7. Burbidge, R., Trotter, M., Buxton, B. et al., Drug design by machine learning: support vector machines for pharmaceutical data analysis. Computers and Chemistry, 2001, 26(1): 5-14.
8. Hong, S. J., Weiss, S. M., Advances in predictive models for data mining. Pattern Recognition Letters, 2001, 22(1): 55-61.
9. Belousov, A. I., Verzakov, S. A., Frese, J. V., A flexible classification approach with optimal generalization performance: support vector machines, Chemom. Intell. Lab. Syst., 2002, 64(1): 15-25.
10. Cai, Y. D., Feng, K. Y., Li, Y. X. et al., Support vector machine for predicting α-turn types, Peptides, 2003, 24(4): 629-630.
11. Gaoa, J. B., Gunnb, S. R., Harrisb, C. J., SVM regression through variational methods and its sequential implementation, Neurocomputing, 2003, 55(1): 151-167.
12. Mei, H., Zhou, Y., Li, S. Z. et al., A new set of amino acid descriptors and its application in peptide QSARs, Biopolymers (Pept. Sci.) 2005, DOI 10.1002bip.20296.
13. Golbraikh, A., Tropsha, A., Beware of q2!, J. Mol. Graphics Mod., 2002, 20(4): 269-276.
14. Tropsha, A., Gramatica, P., Gombar, V. K., The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models, QSAR Comb. Sci., 2003, 22(1):69-77.
15. Gramatica, P., Pilutti, P., Papa, E., Validated QSAR prediction of OH tropospheric degradation of VOCs: splitting into training-test sets and consensus modeling, J. Chem. Inf. Comput. Sci., 2004, 44(5): 1794-1802.
16. Collantes, E. R., Dunn, W. J., Amino acid side chain descriptors for quantitative structure activity relationship studies of peptide analogues, J. Med. Chem., 1995, 38(14): 2705-2713.
17. Hellberg, S., Eriksson, L., Jonsson, J. et al., Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships, Int. J. Pept Protein Res., 1991, 37(5): 414-424.