Recent advances on A 3 adenosine receptor antagonists by QSAR tools

Current Topics in Medicinal Chemistry

Volumn 12, Issue 8, 2012, Pages 878-894

  Luan, Feng ^a,b   Borges, Fernanda ^a   Cordeiro, M Natália D S ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b YANTAI UNIVERSITY   (China)

Author keywords

Antagonists; Human A 3 adenosine receptor subtype; Multiple linear regression; Quantitative structure activity relationships; Radial basis function neural networks

Indexed keywords

ADENOSINE A3 RECEPTOR; ADENOSINE A3 RECEPTOR ANTAGONIST; PYRAZOLONE DERIVATIVE; PYRIMIDINE DERIVATIVE;

ACCURACY; ARTIFICIAL NEURAL NETWORK; BINDING AFFINITY; COMPARATIVE MOLECULAR FIELD ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; DRUG ACTIVITY; DRUG RECEPTOR BINDING; DRUG STRUCTURE; DRUG TARGETING; HUMAN; HYDROGEN BOND; PREDICTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION; REVIEW; STATIC ELECTRICITY; STEREOSPECIFICITY;

ADENOSINE A3 RECEPTOR ANTAGONISTS; HUMANS; MOLECULAR STRUCTURE; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; RECEPTOR, ADENOSINE A3;

EID: 84860307750     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/156802612800166792     Document Type: Review

References (113)

1. Constance, N.; Wilson, S.; Mustafa, J. Adenosine receptors in health and disease (Handbook of Experimental Pharmacology), Volume 193, Springer-Verlag Berlin Heidelberg. 2009.
2. 1 receptor. J. Med. Chem., 2003, 46, 1492-1503.
3. 1 receptor antagonists and the kidney. Curr. Opin. Nephrol. Hypertens., 2003, 12, 497-502.
4. 2A receptors in Parkinson's disease. Drug Dev. Res., 2003, 58, 354-367.
5. Rubino, A.; Ralevic, V.; Burnstock, G. Contribution of P1-(A2b subtype) and P2-purinoceptors to the control of vascular tone in the rat isolated mesenteric arterial bed. Br. J. Pharmacol., 1995, 115, 648-652.
6. 2b adenosine receptor mediates cAMP responses to adenosine receptor agonists in human intestinal epithelia J. Biol. Chem., 1995, 270, 2387-2394.
7. 2B adenosine receptors in human mast cells Drug Dev. Res., 2003, 58, 461-471.
8. 2A adenosine receptor antagonists-future drugs for Parkinson's disease. Drugs Future, 2000, 25, 1043.
9. 2B. Eur. J. Med. Chem., 2004, 39, 879-887.
10. Fredholm, B.B.; IJzerman, A. P, Jacobson K.A.; Klotz, K. N.; Linden, J. International union of pharmacology. XXV. Nomenclature and classification of adenosine receptors. Pharmacol. Rev. 2001, 53, 527-552.
11. Lopes, L.V.; Rebola, N.; Pinheiro, P. C.; Richardson, P. J.; Oliveira, C. R.; Cunha, R. A. Adenosine A3 receptors are located in neurons of the rat hippocampus. Neuroreport., 2003, 14, 1645-1648.
12. Baraldi, P. G.; Tabrizi, M.A.; Gessi, S.; Borea, P. A. Adenosine receptor antagonists: translating medicinal chemistry and pharmacology into clinical utility. Chem. Rev., 2008, 108, 238-263.
13. Jacobson, M. A.; Chakravarty, P. K.; Johnson, R. G.; Norton, R. Molecular, pharmacological, and therapeutic advances: Abstracts from the International Symposium. Drug Dev. Res., 1996, 37, 109-191.
14. van Rhee, A. M.; Jiang, J. L.; Melman, N.; Olah, M. E.; Stiles, G. L.; Jacobson, K. A. A novel class of adenosine A3 receptor ligands. 1. 3-(2-Pyridinyl)isoquinoline derivatives. J. Med. Chem., 1996, 39, 2980-2989.
15. Jiang, J. L.; van Rhee, A. M.; Chang, L.; Patchornik, A.; Ji, X. D.; Evans, P.; Melman, N.; Jacobson, K. A. Structure-activity relationships of 4-(phenylethynyl)-6-phenyl-1,4-dihydropyridines as highly selective A3 adenosine receptor antagonists. J. Med.Chem., 1997, 40, 2596-2608.
16. Li, A. H.; Moro, S.; Melman, N.; Ji, X. D.; Jacobson, K. A. Structure-activity relationships and molecular modeling of 3,5-diacyl-2,4-dialkylpyridine derivatives as selective A3 adenosine receptor antagonists J. Med. Chem., 1998, 41, 3186-3201.
17. 3 adenosine receptor antagonists. J. Med. Chem., 1999, 42, 706-721.
18. 3 adenosine receptor antagonists. Drug Dev. Res., 1999, 48, 171-177.
19. Kim, Y. C.; Ji, X. D.; Jacobson, K. A. Derivatives of the triazoloquinazoline adenosine antagonist (CGS15943) are selective for the human A3 receptor subtype. J. Med. Chem., 1996, 39, 4142-4148.
20. 3 receptor subtypes. J. Med. Chem., 1998, 41, 2835-2845.
21. Karton, Y.; Jiang, J. L.; Ji, X. D.; Melman, N.; Olah, M. E.; Stiles, G. L.; Jacobson, K. A. Synthesis and biological activities of flavonoid derivatives as A3 adenosine receptor antagonists. J. Med. Chem., 1996, 39, 2293-2301.
22. Baraldi, P. G.; Cacciari, B.; Romagnoli, R.; Spalluto, G.; Klotz, KN; Leung, E.; Varani, K.; Gessi, S.; Merighi, S.; Borea, P. A. Pyrazolo[4,3-e]-1,2,4-triazolo[1,5-c]pyrimidine derivatives as highly potent and selective human A3 adenosine receptor antagonists J. Med. Chem., 1999, 42, 4473-4478.
23. 3 adenosine receptor antagonists. J. Med. Chem., 2002, 45, 770-780.
24. Okamura, T.; Kurogi, Y.; Nishikawa, H.; Hashimoto, K.; Fujiwara, H.; Nagao, Y. 1,2,4-Triazolo[5,1-i]purine derivatives as highly potent and selective human adenosine A3 receptor ligands. J. Med. Chem., 2002, 45, 3703-3708.
25. van Muijlwijk-Koezen, J. E.; Timmerman, H.; van der Goot, H.; Menge, W. M. P. B.; Kunzel, J. F. V.; de Groote, M.; IJzerman, A. P. Isoquinoline and quinazoline urea analogues as antagonists for the human adenosine A3 receptor. J. Med. Chem., 2000, 43, 2227-2238.
26. Saki, M.; Tsumuki, H.; Nonaka, H.; Shimada, J.; Ichimura, M. KF26777 (2-(4-bromophenyl)-7,8-dihydro-4-propyl-1Himidazo[2,1-i]purin-5(4H)-one dihydrochloride), a new potent and selective adenosine A3 receptor antagonist. Eur. J. Pharmacol., 2002, 444, 133-141.
27. Priego, E. M.; Kuenzel, J. F. V.; IJzerman, A. P.; Camarasa, M.-J.; Pérez-Pérez, M.-J. Pyrido[2,1-f]purine-2,4-dione derivatives as a novel class of highly potent human A3 adenosine receptor antagonists. J. Med. Chem., 2002, 45, 3337-3344.
28. van Muijlwijk-Koezen, J. E.; Timmerman, H.; Vollinga, R. C.; Kunnzel, J. F. V.; de Groote, M.; Visser, S.; IJzerman, A. P. Thiazole and thiadiazole analogues as a novel class of adenosine receptor antagonists. J. Med. Chem., 2001, 44, 749-762.
29. Pastorin, G.; Federico, S.; Paoletta, S.; Corradino, M.; Cateni, F.; Cacciari, B.; Klotz, K. N.; Gao, Z. G.; Jacobson, K. A.; Spalluto, G.; Moro, S. Synthesis and pharmacological characterization of a new series of 5,7-disubstituted-[1,2,4]triazolo[1,5-a][1,3,5]triazine derivatives as adenosine receptor antagonists: A preliminary inspection of ligand-receptor recognition process. Bioorg. Med. Chem., 2010, 18, 2524-2536.
30. 3 adenosine receptors antagonists: New insights into structure-affinity relationship and receptor-antagonist recognition. J. Med. Chem., 2010, 53, 3361-3375.
31. González, M. P.; Terán, C.; Teijeira, M.; Helguera, A. M. Quantitative structure activity relationships as useful tools for the design of new adenosine receptor ligands. 1. Agonists. Curr. Med. Chem., 2006, 13, 2253-2266.
32. Moro, S.; Deflorian, F.; Bacilieri, M.; Spalluto, G. Novel strategies for the design of new potent and selective human A(3) Receptor Antagonists: An update. Curr. Med. Chem., 2006, 13, 639-645.
33. González, M. P.; Terán, C.; Teijeira, M. Search for new antagonist ligands for adenosine receptors from QSAR point of view. How close are we? Med. Res. Rev., 2008, 28, 329-371.
34. Tintori, C.; Manetti, F.; Botta, M. Pharmacophoric models and 3D QSAR studies of the adenosine receptor ligands. Curr. Top. Med. Chem., 2010, 10, 1019-1035.
35. Moro, S.; van Rhee, A. M.; Sanders, L. H.; Kenneth A. J. Flavonoid derivatives as adenosine receptor antagonists: A comparison of the hypothetical receptor binding site based on a comparative molecular field analysis model. J. Med. Chem., 1998, 41, 46-52.
36. Tchilibon, S.; Kim, S. K.; Gao, Z. G.; Harris, B. A.; Blaustein, J. B.; Gross, A. S.; Duong, H. T.; Melman, N.; Jacobson, K. A. Exploring distal regions of the A3 adenosine receptor binding site: sterically constrained N-6-(2-phenylethyl)adenosine derivatives as potent ligands. Bioorg. Med. Chem., 2004, 12, 2021-2034.
37. 3 receptor subtype. Bioorg. Med. Chem. Lett., 2004, 14, 3705-3709.
38. Bhattacharya, P.; Leonard, J. T.; Roy, K. Exploring QSAR of thiazole and thiadiazole derivatives as potent and selective human adenosine A3 receptor antagonists using FA and GFA techniques. Bioorg. Med. Chem., 2005, 13, 1159-1165.
39. 3 receptor antagonist 1,2,4-triazolo[4,3-a]quinoxalin-1-one derivatives using chemometric tools. Bioorg. Med. Chem. Lett., 2005, 15, 3737-3743.
40. García-Domenech, R.; de Julian-Ortiz, J.V. Antimicrobial activity characterization in a heterogeneous group of compounds. J. Chem. Inf. Comput. Sci., 1998, 38, 445-449.
41. 3 receptors. Bioorg. Med. Chem., 2005, 13, 5330-5337.
42. Katritzky, A. R.; Lobanov, V. S.; Karelson, M. Comprehensive descriptors for structural and statistical analysis, Reference Manual, Version 2.0. 1994.
43. Katritzky, A. R.; Lobanov, V. S.; Karelson, M. CODESSA: Training Manual; University of Florida, Gainesville, FL. 1995.
44. 3 adenosine receptor antagonist. Bioorg. Med. Chem., 2006, 14, 4923-4932.
45. 3 adenosine receptor antagonists: A QSAR study. Int. J. Mol. Sci., 2006, 7, 485-496.
46. 3 adenosine receptor antagonists. Acta Chim. Sinica, 2007, 65, 1420-1426.
47. Sharma, B.K.; Singh, P. Quantitative structure-activity relationship study of human A(3) adenosine receptor antagonists: Derivatives of 2-aryl-1,2,4-triazolo [4,3-alpha]quinoxaline. J. Enzym. Inhib. Med. Chem., 2007, 22, 165-169.
48. Baraldi, P.G.; Preti, D.; Tabrizi, M.A.; Romagnoli, R.; Saponaro, G.; Baraldi, S.; Botta, M.; Bernardini, C.; Tafi, A.; Tuccinardi, T.; Martinelli, A.; Varani, K.; Borea, P. A. Structure-activity relationship studies of a new series of imidazo[2,1-f] purinones as potent and selective A3 adenosine receptor antagonists. Bioorg. Med. Chem., 2008, 16, 10281-10294.
49. 3 adenosine receptor antagonists. Synthesis, pharmacological evaluation, and ligand-receptor modeling studies. J. Med. Chem., 2009, 52, 2407-2419.
50. Moro, S.; Bacilieri M.; Ferrari, C.; Spalluto, Giampiero. Autocorrelation of molecular electrostatic potential surface properties combined with partial least squares analysis as alternative attractive tool to generate ligand-based 3D-QSARs. Curr. Drug. Discov. Techonol., 2005, 2, 13-21.
51. ChemBioOffice 11.0, Cambridge Software, Cambridge, MA, 2008.
52. HyperChem 4.0, Hypercube, Inc., 1994.
53. Stewart, J. P. P. MOPAC 6.0, Quantum Chemistry Program Exchange, QCPE, No. 455, Indiana University, Bloomington, IN. 1989.
54. Put, R.; Daszykowski, M.; Baczek, T.; Heyden, Y. V. Retention prediction of peptides based on uninformative variable elimination by partial least squares. J. Proteom. Res., 2006, 5, 1618-1625.
55. Massart, D. L.; Vandeginste, B. G. M.; Buydens, L. M. C.; de Jong, S.; Lewis, P. J.; Smeyers-Verbeke, J. Handbook of Chemometrics and Qualimetrics - Part A, Elsevier Science, Amsterdam. 1997.
56. Livingstone, D. J.; Salt, D. W. Judging the significance of multiple linear regression models. J. Med. Chem., 2005, 48, 661-663.
57. Gharagheizi, F. QSPR analysis for intrinsic viscosity of polymer solutions by means of GA-MLR and RBFNN. Comput. Mater. Sci., 2007, 40, 159-167.
58. Derks, E. P. P. A; Sanchez-Pastor, M. S.; Buydens, L. M. Robustness analysis of radial basis function and multi-layered feedforward neural network models. Chemom. Intell. Lab. Syst., 1995, 28, 49-60.
59. Orr, M.J.L. MATLAB Routines for subset selection and ridge regression in linear neural networks, Centre for cognitive science, Edinburgh University, 1996.
60. Orr, M. J. L. Introduction to radial basis function networks, Centre for Cognitive Science, Edinburgh University, 1996.
61. SPSS Base 8.0 for Windows User's Guide. SPSS Inc., Chicago, 1998.
62. Jalali-Heravi, M.; Fatemi, M.H. Prediction of thermal conductivity detection response factors using an artificial neural network. J. Chromatogr. A, 2000, 897, 227-235.
63. Topliss, J. G.; Edwards, R. P. Chance factors in studies of quantitative structure-activity relationships. J. Med. Chem., 1979, 22, 1238-1244.
64. [64] Principles for the Validation, for Regulatory Purposes, of (Quantitative) Structure-Activity Relationship Models. OECD, Paris, France, 2004. http://www.oecd.org/document/23/0,2340,en_26-49_201185_33957015_1_1_1_1,00.html
65. Netzeva, T. I., Worth, A. P., Aldenberg, T., Benigni, R., Cronin, M. T. D., Gramatica, P., Jaworska, J. S., Kahn, S., Klopman, G., Marchant, C. A., Myatt, G., Nikolova-Jeliazkova, N., Patlewicz, G. Y., Perkins, R., Roberts, D. W., Schultz, T. W., Stanton, D. T., van de Sandt, J. J. M., Tong, W., Veith, G., Yang, C. Current status of methods for defining the applicability domain of (quantitative) structure-activity relationships. The report and recommendations of ECVAM workshop 52. ATLA, 2005, 33, 155-173.
66. González-Diaz, H., Vilar, S., Santana, L., Podda, G., Uriarte, E. On the applicability of QSAR for recognition of miRNA bioorganic structures at early stages of organism and cell development: Embryo and stem cells. Bioorg. Med. Chem., 2007, 15, 2544-2550.
67. Helguera, A.M., González, M.P., Cordeiro, M. N. D. S., Cabrera, M. A. Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds. Toxicol. Appl. Pharmacol., 2007, 221, 189-202.
68. Papa, E.; Kovarich, S.; Gramatica, P. Development, validation and inspection of the applicability domain of QSPR models for physicochemical properties of polybrominated diphenyl ethers. QSAR Comb. Sci., 2008, 28, 790-796
69. Caballero, J.; Fernandez, M. Artificial neural networks from MATLAB in medicinal chemistry. Bayesian-regularized genetic neural networks (BRGNN): application to the prediction of the antagonistic activity against human platelet thrombin receptor (PAR-1). Curr. Top. Med. Chem., 2008, 8, 1580-1605.
70. Duardo-Sanchez, A.; Patlewicz, G.; Lopez-Diaz, A. Current topics on software use in medicinal chemistry: intellectual property, taxes, and regulatory issues. Curr. Top. Med. Chem., 2008, 8, 1666-1675.
71. Gonzalez, M.P.; Teran, C.; Saiz-Urra, L.; Teijeira, M. Variable selection methods in QSAR: an overview. Curr. Top. Med. Chem., 2008, 8, 1606-1627.
72. Gonzalez-Diaz, H. Quantitative studies on Structure-Activity and Structure-Property Relationships (QSAR/QSPR). Curr. Top. Med. Chem., 2008, 8, 1554.
73. Gonzalez-Diaz, H.; Prado-Prado, F.; Ubeira, F.M. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr. Top. Med. Chem., 2008, 8, 1676-1690.
74. Helguera, A.M.; Combes, R.D.; Gonzalez, M.P.; Cordeiro, M.N.D.S. Applications of 2D descriptors in drug design: a DRAGON tale. Curr. Top. Med. Chem., 2008, 8, 1628-1655.
75. Ivanciuc, O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr. Top. Med. Chem., 2008, 8, 1691-1709.
76. Vilar, S.; Cozza, G.; Moro, S. Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Curr. Top. Med. Chem., 2008, 8, 1555-1572.
77. Wang, J.F.; Wei, D.Q.; Chou, K.C. Drug candidates from traditional chinese medicines. Curr. Top. Med. Chem., 2008, 8, 1656-1665.
78. Wang, J.F.; Wei, D.Q.; Chou, K.C. Pharmacogenomics and personalized use of drugs. Curr. Top. Med. Chem., 2008, 8, 1573-1579.
79. Chen, J.; Shen, B. Computational analysis of amino acid mutation: a proteome wide perspective. Curr. Proteom., 2009, 6, 228-234.
80. Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteom., 2009, 6, 262-274.
81. Giuliani, A.; Di Paola, L.; Setola, R. Proteins as networks: A mesoscopic approach using haemoglobin molecule as case study. Curr. Proteom., 2009, 6, 235-245.
82. González-Díaz, H.; Prado-Prado, F.; Pérez-Montoto, L.G.; Duardo-Sánchez, A.; López-Díaz, A. QSAR models for proteins of parasitic organisms, plants and human guests: theory, applications, legal protection, taxes, and regulatory issues. Curr. Proteom., 2009, 6, 214-227.
83. Ivanciuc, O. Machine learning quantitative structure-activity relationships (QSAR) for peptides binding to Human Amphiphysin-1 SH3 domain. Curr. Proteom., 2009, 4, 289-302.
84. Pérez-Montoto, L.G.; Prado-Prado, F.; Ubeira, F.M.; González-Díaz, H. Study of parasitic infections, cancer, and other diseases with mass-spectrometry and quantitative proteome-disease relationships. Curr. Proteom., 2009, 6, 246-261.
85. Torrens, F.; Castellano, G. Topological charge-transfer indices: from small molecules to proteins. Curr. Proteom., 2009, 6(4), 204-213.
86. Vázquez, J.M.; Aguiar, V.; Seoane, J.A.; Freire, A.; Serantes, J.A.; Dorado, J.; Pazos, A.; Munteanu, C.R. Star graphs of protein sequences and proteome mass spectra in cancer prediction. Curr. Proteom., 2009, 6, 275-288.
87. Chou, K.C. Graphic rule for drug metabolism systems. Curr Drug Metab., 2010, 11, 369-378.
88. Garcia, I.; Diop, Y.F.; Gomez, G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr. Drug Metab., 2010, 11, 307-314.
89. Gonzalez-Diaz, H. Network topological indices, drug metabolism, and distribution. Curr. Drug Metab., 2010, 11, 283-284.
90. Gonzalez-Diaz, H.; Duardo-Sanchez, A.; Ubeira, F.M.; Prado-Prado, F.; Perez-Montoto, L.G.; Concu, R.; Podda, G.; Shen, B. Review of MARCH-INSIDE & complex networks prediction of drugs: ADMET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr. Drug Metab., 2010, 11, 379-406.
91. Khan, M.T. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr. Drug Metab., 2010, 11, 285-295.
92. Martinez-Romero, M.; Vazquez-Naya, J.M.; Rabunal, J.R.; Pita-Fernandez, S.; Macenlle, R.; Castro-Alvarino, J.; Lopez-Roses, L.; Ulla, J.L.; Martinez-Calvo, A.V.; Vazquez, S.; Pereira, J.; Porto-Pazos, A.B.; Dorado, J.; Pazos, A.; Munteanu, C.R. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr. Drug Metab., 2010, 11, 347-368.
93. Mrabet, Y.; Semmar, N. Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts. Curr. Drug Metab., 2010, 11, 315-341.
94. Wang, J.F.; Chou, K.C. Molecular modeling of cytochrome P450 and drug metabolism. Curr. Drug Metab., 2010, 11, 342-346.
95. Zhong, W.Z.; Zhan, J.; Kang, P.; Yamazaki, S. Gender specific drug metabolism of PF-02341066 in rats-role of sulfoconjugation. Curr. Drug Metab., 2010, 11, 296-306.
96. Concu, R.; Podda, G.; Ubeira, F.M.; Gonzalez-Diaz, H. Review of QSAR models for enzyme classes of drug targets: theoretical background and applications in parasites, hosts, and other organisms. Curr. Pharm. Des., 2010, 16, 2710-2723.
97. Estrada, E.; Molina, E.; Nodarse, D.; Uriarte, E. Structural contributions of substrates to their binding to P-Glycoprotein. A TOPSMODE approach. Curr. Pharm. Des., 2010, 16, 2676-2709.
98. Garcia, I.; Fall, Y.; Gomez, G. QSAR, Docking, and CoMFA studies of GSK3 inhibitors. Curr. Pharm. Des., 2010, 16, 2666-2675.
99. Marrero-Ponce, Y.; Casanola-Martin, G.M.; Khan, M.T.; Torrens, F.; Rescigno, A.; Abad, C. Ligand-based computer-aided discovery of tyrosinase inhibitors. Applications of the TOMOCOMDCARDD method to the elucidation of new compounds. Curr. Pharm. Des., 2010, 16, 2601-2624.
100. Munteanu, C.R.; Fernandez-Blanco, E.; Seoane, J.A.; Izquierdo-Novo, P.; Rodriguez-Fernandez, J.A.; Prieto-Gonzalez, J.M.; Rabunal, J.R.; Pazos, A. Drug discovery and design for complex diseases through QSAR computational methods. Curr. Pharm. Des., 2010, 16, 2640-2655.
101. Roy, K.; Ghosh, G. Exploring QSARs with extended topochemical atom (ETA) indices for modeling chemical and drug toxicity. Curr. Pharm. Des., 2010, 16, 2625-2639.
102. Speck-Planche, A.; Scotti, M.T.; de Paulo-Emerenciano, V. Current Pharmaceutical Design of antituberculosis drugs: future perspectives. Curr. Pharm. Des., 2010, 16, 2656-2665.
103. Vazquez-Naya, J.M.; Martinez-Romero, M.; Porto-Pazos, A.B.; Novoa, F.; Valladares-Ayerbes, M.; Pereira, J.; Munteanu, C.R.; Dorado, J. Ontologies of drug discovery and design for neurology, cardiology and oncology. Curr. Pharm. Des., 2010, 16, 2724-2736.
104. Chiş, O.; Dumitru, O.; Concu, R.; Shen, B. Reviewing yeast network and report of new stochastic-credibility cell cycle models. Curr. Bioinform., 2011, 6, 35-43.
105. Dave, K.; Banerjee, A. Bioinformatics analysis of functional relations between CNPs regions. Curr. Bioinform., 2011, 6, 122-128.
106. Duardo-Sanchez, A.; Patlewicz, G.; González-Díaz, H. A review of network topological indices from chem-bioinformatics to legal sciences and back. Curr. Bioinform., 2011, 6, 53-70.
107. García, I.; Fall, Y.; Gómez, G. Trends in bioinformatics and chemoinformatics of vitamin D analogues and their protein targets. Curr. Bioinform., 2011, 6, 16-24.
108. Ivanciuc, T.; Ivanciuc, O.; Klein, D.J. Network-QSAR with reaction poset quantitative superstructure-activity relationships (QSSAR) for PCB chromatographic properties. Curr. Bioinform., 2011, 6, 25-34.
109. Prado-Prado, F.; Escobar-Cubiella, M.; García-Mera, X. Review of Bioinformatics and QSAR studies of β-secretase inhibitors. Curr. Bioinform., 2011, 6, 3-15.
110. Riera-Fernández, P.; Munteanu, C.R.; Pedreira-Souto, N.; Duardo-Sanchez, A.; González-Díaz, H. Definition of Markov-harary invariants and review of classic topological indices and databases of biology, parasitology, technology, and social-legal networks. Curr. Bioinform., 2011, 6, 94-121.
111. Speck-Planche, A.; Cordeiro, M.N.D.S. Application of Bioinformatics for the search of novel anti-viral therapies: rational design of anti-herpes agents. Curr. Bioinform., 2011, 6, 81-93.
112. Wan, S.B.; Hu, L.L.; Niu, S.; Wang, K.; Cai, Y.D.; Lu, W.C.; Chou, K.C. Identification of multiple subcellular locations for proteins in budding yeast. Curr. Bioinform., 2011, 6, 71-80.
113. Bhattacharjee, B.; Jayadeepa, R.M.; Banerjee, S.; Joshi, J.; Middha, S.K.; Mole, J.P.; Samuel, J. Review of complex network and gene ontology in pharmacology approaches: mapping natural compounds on potential drug target colon cancer network. Curr. Bioinform., 2011, 6, 44-52.