SVM model for virtual screening of Lck inhibitors

Journal of Chemical Information and Modeling

Volumn 49, Issue 4, 2009, Pages 877-885

  Liew, Chin Y ^a   Ma, Xiao H ^a   Liu, Xianghui ^a   Yap, Chun W ^a  

^a NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; DIAGNOSIS; DIGITAL LIBRARIES;

AUTOIMMUNE DISEASE; CHEMICAL LIBRARIES; FALSE POSITIVE RATES; INHIBITORY ACTIVITY; PROTEIN TYROSINE KINASE; SVM MODEL; TRANSPLANT REJECTION; VIRTUAL SCREENING;

SUPPORT VECTOR MACHINES;

ENZYME INHIBITOR; PROTEIN KINASE LCK;

ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHEMICAL MODEL; CHEMISTRY; COMPUTER SIMULATION; DOCUMENTATION; DRUG ANTAGONISM; DRUG DESIGN; DRUG SCREENING; METHODOLOGY; REPRODUCIBILITY; STATISTICAL MODEL; STRUCTURE ACTIVITY RELATION; SYNTHESIS;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DRUG DESIGN; DRUG EVALUATION, PRECLINICAL; ENZYME INHIBITORS; LOGISTIC MODELS; LYMPHOCYTE SPECIFIC PROTEIN TYROSINE KINASE P56(LCK); MODELS, CHEMICAL; NEURAL NETWORKS (COMPUTER); REPRODUCIBILITY OF RESULTS; STRUCTURE-ACTIVITY RELATIONSHIP; SUBJECT HEADINGS;

EID: 66149099982     PISSN: 15499596     EISSN: 1549960X     Source Type: Journal    
DOI: 10.1021/ci800387z     Document Type: Article

References (49)

1. Veillette, A.; Abraham, N.; Caron, L.; Davidson, D. The lymphocyte-specific tyrosine protein kinase p561ck. Semin. Immunol. 1991, 3 (3), 143-52.
2. Biondi, A.; Paganin, C.; Rossi, V.; Benvestito, S.; Perlmutter, R. M.; Mantovani, A.; Allavena, P. Expression of lineage-restricted protein tyrosine kinase genes in human natural killer cells. Eur. J. Immunol. 1991, 21 (3), 843-6.
3. Weiss, A.; Liftman, D. R. Signal transduction by lymphocyte antigen receptors. Cell 1994, 76 (2), 263-74.
4. Isakov, N.; Wange, R. L.; Samelson, L. E. The role of tyrosine kinases and phosphotyrosine-containing recognition motifs in regulation of the T cell-antigen receptor-mediated signal transduction pathway. J. Leukocyte Biol. 1994, 55 (2), 265-71.
5. Shaw, A. S.; Amrein, K. E.; Hammond, C.; Stern, D. F.; Sefton, B. M.; Rose, J. K. The lck tyrosine protein kinase interacts with the cytoplasmic tail of the CD4 glycoprotein through its unique amino-terminal domain. Cell 1989, 59 (4), 627-36.
6. Trevillyan, J. M.; Chiou, X. G.; Ballaron, S. J.; Tang, Q. M.; Buko, A.; Sheets, M. P.; Smith, M. L.; Putman, C. B.; Wiedeman, P.; Tu, N.; Madar, D.; Smith, H. T.; Gubbins, E. J.; Warrior, U. P.; Chen, Y. W.; Mollison, K. W.; Faltynek, C. R.; Djuric, S. W. Inhibition of p56(lck) tyrosine kinase by isothiazolones. Arch. Biochem. Biophys. 1999, 364 (1), 19-29.
7. Palacios, E. H.; Weiss, A. Function of the Src-family kinases, Lck and Fyn, in T-cell development and activation. Oncogene 2004, 23 (48), 7990-8000.
8. Kamens, J. S.; Ratnofsky, S. E.; Hirst, G. C. Lck inhibitors as a therapeutic approach to autoimmune disease and transplant rejection. Curr. Opin. Invest. Drugs 2001, 2 (9), 1213-9.
9. Fischer, P. M. Computational chemistry approaches to drug discovery in signal transduction. Biotechnol. J. 2008, 3 (4), 452-70.
10. Seifert, M. H.; Lang, M. Essential factors for successful virtual screening. Mini-Rev. Med. Chem. 2008, 8 (1), 63-72.
11. Klebe, G. Virtual ligand screening: strategies, perspectives and limitations. Drug Discovery Today 2006, 11 (13-14), 580-94.
12. Novic, M.; Nikolovska-Coleska, Z.; Solmajer, T. Quantitative Structure-Activity Relationship of Flavonoid p561ck Protein Tyrosine Kinase Inhibitors. A Neural Network Approach. J. Chem. Inf. Comput. Sci. 1997, 37 (6), 990-998.
13. Nikolovska-Coleska, Z.; Suturkova, L.; Dorevski, K.; Krbavcic, A.; Solmajer, T. Quantitative structure-activity relationship of flavonoid inhibitors of p56(lck) protein tyrosine kinase: A Classical/Quantum chemical approach. Quant. Struct.-Act. Relat. 1998, 17 (1), 7-13.
14. Zupan, J.; Novic, M. Optimisation of structure representation for QSAR studies. Anal. Chim. Acta 1999, 388 (3), 243-250.
15. Oblak, M.; Randic, M.; Solmajer, T. Quantitative structure-activity relationship of flavonoid analogues. 3. Inhibition of p561ck protein tyrosine kinase. J. Chem. Inf. Comput. Sci. 2000, 40 (4), 994-1001.
16. Thakur, A.; Vishwakarma, S.; Thakur, M. QSAR study of flavonoid derivatives as p561ck tyrosinkinase inhibitors. Bioorg. Med. Chem. 2004, 12 (5), 1209-14.
17. Chen, P.; Doweyko, A. M.; Norris, D.; Gu, H. H.; Spergel, S. H.; Das, J.; Moquin, R. V.; Lin, J.; Wityak, J.; Iwanowicz, E. J.; McIntyre, K. W.; Shuster, D. J.; Behnia, K.; Chong, S.; de Fex, H.; Pang, S.; Pitt, S.; Shen, D. R.; Thrall, S.; Stanley, P.; Kocy, O. R.; Witmer, M. R.; Kanner, S. B.; Schieven, G. L.; Barrish, J. C. Imidazoqui-noxaline Src-family kinase p56Lck inhibitors: SAR, QSAR, and the discovery of (S)-N-(2-chloro-6-methylphenyl)-2-(3-methyl-1- piper-azinyl)imidazo- [1,5-a]pyrido[3,2-e]pyrazin-6-amine (BMS-279700) as a potent and orally active inhibitor with excellent in vivo antiinflammatory activity. J. Med. Chem. 2004, 47 (18), 4517-29.
18. Badiger, A. M.; Noolvi, M. N.; Nayak, P. V. QSAR study of benzothiazole derivatives as p561ck inhibitors. Lett. Drug Des. Discovery 2006, 3 (8), 550-560.
19. Bharatham, N.; Bharatham, K.; Lee, K. W. P56 LCK inhibitor identification by pharmacophore modelling and molecular docking. Bull. Korean Chem. Soc. 2007, 28 (2), 200-206.
20. Tominaga, Y.; Jorgensen, W. L. General model for estimation of the inhibition of protein kinases using Monte Carlo simulations. J. Med. Chem. 2004, 47 (10), 2534-49.
21. Gramatica, P. Principles of QSAR models validation: internal and external. QSAR Comb. Sci. 2007, 26 (5), 694-701.
22. Parker, C. N.; Bajorath, J. Towards Unified Compound Screening Strategies: A Critical Evaluation of Error Sources in Experimental and Virtual High-Throughput Screening. QSAR Comb. Sci. 2006, 25 (12), 1153-1161.
23. Han, L. Y.; Ma, X. H.; Lin, H. H.; Jia, J.; Zhu, F.; Xue, Y.; Li, Z. R.; Cao, Z. W.; Ji, Z. L.; Chen, Y. Z. A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor. J. Mol. Graphics Modell. 2008, 26 (8), 1276-1286.
24. Ma, X. H.; Wang, R.; Yang, S. Y.; Li, Z. R.; Xue, Y.; Wei, Y. C.; Low, B. C.; Chen, Y. Z. Evaluation of Virtual Screening Performance of Support Vector Machines Trained by Sparsely Distributed Active Compounds. J. Chem. Inf. Model. 2008, 48 (6), 1227-1237.
25. Li, H.; Yap, C. W.; Ung, C. Y.; Xue, Y.; Li, Z. R.; Han, L. Y.; Lin, H. H.; Chen, Y. Z. Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. J. Pharm. Sci. 2007, 96 (11), 2838-2860.
26. Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, 1995; p xv, 188.
27. Doucet, J.-P.; Barbault, F.; Xia, H.; Panaye, A.; Fan, B. Nonlinear SVM Approaches to QSPR/QSAR Studies and Drug Design. Curr. Comput.-Aided Drug Des. 2007, 3, 263-289.
28. Xue, Y.; Yap, C. W.; Sun, L. Z.; Cao, Z. W.; Wang, J. F.; Chen, Y. Z. Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach. J. Chem. Inf. Comput. Sci. 2004, 44 (4), 1497-1505.
29. Glick, M.; Jenkins, J. L.; Nettles, J. H.; Hitchings, H.; Davies, J. W. Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and Laplacian-modified naive Bayesian classifiers. J. Chem. Inf. Model. 2006, 46 (1), 193-200.
30. Lepp, Z.; Kinoshita, T.; Chuman, H. Screening for new antidepressant leads of multiple activities by support vector machines. J. Chem. Inf. Model. 2006, 46 (1), 158-167.
31. Chen, B.; Harrison, R. F.; Papadatos, G.; Willett, P.; Wood, D. J.; Lewell, X. Q.; Greenidge, P.; Stiefl, N. Evaluation of machine-learning methods for ligand-based virtual screening. J. Comput.-Aided Mol. Des. 2007, 21 (1-3), 53-62.
32. Oprea, T. L.; Gottfries, J. Chemography: The art of navigating in chemical space. J. Comb. Chem. 2001, 3 (2), 157-166.
33. Bocker, A.; Schneider, G.; Teckentrup, A. NIPALSTREE: A new hierarchical clustering approach for large compound libraries and its application to virtual screening. J. Chem. Inf. Model. 2006, 46 (6), 2220-2229.
34. Xue, Y.; Li, Z. R.; Yap, C. W.; Sun, L. Z.; Chen, X.; Chen, Y. Z. Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents. J. Chem. Inf. Comput. Sci. 2004, 44 (5), 1630-1638.
35. Fink, T.; Raymond, J.-L. Virtual exploration of the chemical universe up to 11 atoms of C, N, O, F: Assembly of 26.4 million structures (110.9 million stereoisomers) and analysis for new ring systems, stereochemistry, physicochemical properties, compound classes, and drug discovery. J. Chem. Inf. Model. 2007, 47 (2), 342-353.
36. Koch, M. A.; Schuffenhauer, A.; Scheck, M.; Wetzel, S.; Casaulta, M.; Odermatt, A.; Ertl, P.; Weidmann, H. Charting biologically relevant chemical space: A structural classification of natural products (SCONP). Proc. Natl. Acad. Sci. U. S. A. 2005, 102 (48), 17272-17277.
37. CambridgeSoft Desktop Software - ChemDraw (Windows/Mac), http://www.cambridgesoft.com/ (accessed Dec 29, 2008).
38. CORINA: Generation of 3D coordinates. http://www.molecular-networks.com/ software/corina/index.html (accessed Dec 29, 2008).
39. Li, Z. R.; Han, L. Y.; Chen, Y. Z. MODEL Reference Manual. http://jing.cz3.nus.edu.sg/model/ (accessed Dec 29, 2008).
40. Perez, J. J. Managing molecular diversity. Chem. Soc. Rev. 2005, 34 (2), 143-52.
41. Willett, P.; Barnard, J. M.; Downs, G. M. Chemical Similarity Searching. J. Chem. Inf. Comput. Sci. 1998, 38 (6), 983-996.
42. Tan, P.-N.; Steinbach, M.; Kumar, V., Classification: Alternative Techniques. In Introduction to Data Mining; Addison-Wesley: 2005; pp 207-315.
43. Jaworska, J.; Nikolova-Jeliazkova, N.; Aldenberg, T. QSAR applicability domain estimation by projection of the training set descriptor space: a review. ATLA Altern. Lab. Anim. 2005, 33 (5), 445-59.
44. Baldi, P.; Brunak, S.; Chauvin, Y.; Andersen, C. A.; Nielsen, H. Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 2000, 16 (5), 412-424.
45. Matthews, B. W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta. 1975, 405 (2), 442-51.
46. Nicholls, A. What do we know and when do we know it. J. Comput.- Aided Mol. Des. 2008, 22 (3-4), 239-55.
47. Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Delivery Rev. 1997, 23, 3-25.
48. Teague, S. J.; Davis, A. M.; Leeson, P. D.; Oprea, T. The Design of Leadlike Combinatorial Libraries. Angew. Chem. 1999, 38 (24), 3743-3748.
49. Yap, C. W.; Xue, Y.; Li, H.; Li, Z. R.; Ung, C. Y.; Han, L. Y.; Zheng, C. J.; Cao, Z. W.; Chen, Y. Z. Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods. Mini. Rev. Med. Chem. 2006, 6 (4), 449-59.