LigSeeSVM: Ligand-based virtual Screening using Support Vector Machines and data fusion

International Journal of Computational Biology and Drug Design

Volumn 4, Issue 3, 2011, Pages 274-289

  Chen, Yen Fu ^a   Hsu, Kai Cheng ^a   Lin, Po Tsun ^a   Frank Hsu, D ^b   Kristal, Bruce S ^c,d   Yang, Jinn Moon ^a  

^a NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^b FORDHAM UNIVERSITY   (United States)

^c BRIGHAM AND WOMEN S HOSPITAL   (United States)

^d HARVARD MEDICAL SCHOOL   (United States)

Author keywords

Data fusion; Ligand based virtual screening; Oestrogen receptor; Rank combination; Support Vector Machines; Thymidine kinase substrates

Indexed keywords

4 AMINOBUTYRIC ACID A RECEPTOR; G PROTEIN COUPLED RECEPTOR; HISTAMINE RECEPTOR; LIGAND; MUSCARINIC RECEPTOR; ORGANOLEAD COMPOUND; SEROTONIN RECEPTOR; THYMIDINE KINASE INHIBITOR; ESTROGEN RECEPTOR; THYMIDINE KINASE;

ARTICLE; COMPUTER MODEL; COMPUTER PREDICTION; CONTROLLED STUDY; DRUG SCREENING; LIGSEESVM MODEL; PHYSICOCHEMICAL MODEL; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; BIOLOGY; COMBINATORIAL CHEMISTRY; DRUG ANTAGONISM; DRUG DEVELOPMENT; DRUG POTENTIATION; FACTUAL DATABASE; METABOLISM; METHODOLOGY; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMBINATORIAL CHEMISTRY TECHNIQUES; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DRUG DISCOVERY; RECEPTORS, ESTROGEN; ROC CURVE; THYMIDINE KINASE;

EID: 80555149711     PISSN: 17560756     EISSN: 17560764     Source Type: Journal    
DOI: 10.1504/IJCBDD.2011.041415     Document Type: Article

References (19)

1. Bissantz, C., Folkers, G. and Rognan, D. (2000) 'Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations', Journal of Medicinal Chemistry, Vol. 43, pp. 4759-4767. (Pubitemid 32002687)
2. Carhart, R. E., Smith, D. H. and Venkataraghavan, R. (1985) 'Atom pairs as molecular features in structure-active studies: definition and applications', Journal of Chemical Information and Computer Sciences, Vol. 25, pp. 64-73.
3. Cerius2 4.8 (2003) Accelrys Inc., San Diego, USA; http://www.accelrys. com/
4. Chang, C. C. and Lin, C. J. (2011) 'LIBSVM: a library for support vector machines', ACM Transactions on Intelligent Systems and Technology, Vol. 2, pp. 27:1-27:27.
5. Hsu, D. F. and Taksa, I. (2005) 'Comparing rank and score combination methods for data fusion in information retrieval', Information Retrieval, Vol. 8, pp. 449-480. (Pubitemid 40550482)
6. Hsu, D. F., Chung, Y-S. and Kristal, B. S. (2006) 'Combinatorial fusion analysis: methods and practices of combining multiple scoring systems', in Hus, H-H. (Ed.): Advanced Data Mining Technologies in Bioinformatics, Idea Group, Inc., pp. 32-62.
7. Jain, A. N. (2004) 'Ligand-based structural hypotheses for virtual screening', Journal of Medicinal Chemistry, Vol. 47, pp. 947-961. (Pubitemid 38176797)
8. Karelson, M., Lobanov, V. S. and Katritzky, A. R. (1996) 'Quantum-chemical descriptors in QSAR/QSPR studies', Chemical Reviews, Vol. 96, pp. 1027-1044.
9. Katritzky, A. R. and Gordeeva, E. V. (1993) 'Traditional topological indices vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research', Journal of Chemical Information and Computer Sciences, Vol. 33, pp. 835-857.
10. Lyne, P. D. (2002) 'Structure-based virtual screening: an overview', Drug Discovery Today, Vol. 7, pp. 1047-1055.
11. Renfrey, S. and Featherstone, J. (2002) 'Structural proteomics', Nature Reviews. Drug Discovery, Vol. 1, pp. 175, 176.
12. 2A) antagonists using self-organizing maps', Journal of Combinatorial Chemistry, Vol. 5, pp. 233-237. (Pubitemid 37140569)
13. Shen, M., LeTiran, A., Xiao, Y., Golbraikh, A., Kohn, H. and Tropsha, A. (2002) 'Quantitative structure-activity relationship analysis of functionalized amino acid anticonvulsant agents using k nearest neighbor and simulated annealing PLS methods', Journal of Medicinal Chemistry, Vol. 45, pp. 2811-2823. (Pubitemid 34627651)
14. Sneath, P. H. A. and Snokal, R. R. (1973) 'Unweighted pair-group method using arithmetic averages', Numerical Taxonomy, pp. 230-234.
15. van Lipzig, M. M., ter Laak, A. M., Jongejan, A., Vermeulen, N. P., Wamelink, M., Geerke, D. and Meerman, J. H. (2004) 'Prediction of ligand binding affinity and orientation of xenoestrogens to the estrogen receptor by molecular dynamics simulations and the linear interaction energy method', Journal of Medicinal Chemistry, Vol. 47, pp. 1018-1030. (Pubitemid 38176805)
16. Xue, Y., Li, Z. R., Yap, C. W., Sun, L. Z., Chen, X. and Chen, Y. Z. (2004) 'Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents', Journal of Chemical Information and Computer Sciences, Vol. 44, pp. 1630-1638.
17. Yang, J. M., Chen, Y-F., Shen, T-W., Kristal, B. S. and Hsu, D. F. (2005) 'Consensus scoring criteria for improving enrichment in virtual screening', Journal of Chemical Information and Modeling, Vol. 8, pp. 449-480.
18. Zernov, V. V., Balakin, K. V., Ivaschenko, A. A., Savchuk, N. P. and Pletnev, I. V. (2003) 'Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions', Journal of Chemical Information and Computer Sciences, Vol. 43, pp. 2048-2056.
19. Zheng, W. and Tropsha, A. (2000) 'Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle', Journal of Chemical Information and Computer Sciences, Vol. 40, pp. 185-194.