From chemical graphs in computer-aided drug design to general Markov-Galvez indices of drug-target, proteome, drug-parasitic disease, technological, and social-legal networks

Current Computer-Aided Drug Design

Volumn 7, Issue 4, 2011, Pages 315-337

  Riera Fernández, Pablo ^a   Munteanu, Cristian R ^b   Dorado, Julian ^b   Martín Romalde, Raquel ^a   Duardo Sanchez, Aliuska ^a   González Díaz, Humberto ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

^b UNIVERSITY OF A CORUÑA   (Spain)

Author keywords

Biosystems; Complex networks; Diseasome; Drug disease networks; Drug target networks; Food webs; Host parasite networks; Markov chains; QSAR; Social legal networks; Topological indices

Indexed keywords

BOND STRENGTH (CHEMICAL); CHAINS; COMPUTATIONAL CHEMISTRY; INDUSTRIAL RESEARCH; MARKOV PROCESSES; MOLECULAR STRUCTURE; PROBLEM SOLVING; PROTEINS; TOPOLOGY;

BIO-SYSTEMS; DISEASOME; DRUG TARGETS; DRUG-DISEASE NETWORK; DRUG-TARGET NETWORK; FOOD WEBS; HOST-PARASITE NETWORK; PARASITE-; QSAR; SOCIAL-LEGAL NETWORK; TOPOLOGICAL INDEX;

COMPLEX NETWORKS;

HISTONE ACETYLTRANSFERASE; HISTONE DEACETYLASE; MAMMALIAN TARGET OF RAPAMYCIN COMPLEX 1; PROTEOME;

BACILLUS ANTHRACIS; BACILLUS SUBTILIS; CHEMICAL STRUCTURE; CHROMATIN; COMPUTER AIDED DESIGN; DRUG RESEARCH; DRUG TARGETING; ENVIRONMENTAL EXPOSURE; ESCHERICHIA COLI; FASCIOLIASIS; METABOLIC REGULATION; MOLECULAR INTERACTION; NONHUMAN; PROTEIN INTERACTION; QUANTITATIVE STRUCTURE PROPERTY RELATION; REVIEW; SACCHAROMYCES CEREVISIAE; SIGNAL TRANSDUCTION; SOCIAL NETWORK;

EID: 82955246111     PISSN: 15734099     EISSN: 18756697     Source Type: Journal    
DOI: 10.2174/157340911798260340     Document Type: Review

References (171)

1. González-Díaz, H.; Munteanu, C.R. Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks. In Transworld Research Network Kerala, India, 2010; pp 001-212.
2. González-Díaz, H.; Prado-Prado, F.; García-Mera, X. Complex Network Entropy: From Molecules to Biology, Parasitology, Technology, Social, Legal, and Neurosciences. In Transworld Research Network: Kerala, India, 2011; pp 001-142.
3. González-Díaz, H.; González-Díaz, Y.; Santana, L.; Ubeira, F.M.; Uriarte, E. Proteomics, networks and connectivity indices. Proteomics, 2008, 8, 750-778.
4. Bornholdt, S.; Schuster, H. G. Handbook of Graphs and Complex Networks: From the Genome to the Internet, WILEY-VCH GmbH & CO. KGa.: Wheinheim, 2003.
5. 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. Proteomics, 2009, 6, 246-261.
6. 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. Proteomics, 2009, 6, 214-227.
7. Chen, J.; Shen, B. Computational analysis of amino acid mutation: a proteome wide perspective. Curr. Proteomics, 2009, 6, 228-234.
8. Chou, K.C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteomics, 2009, 6, 262-274.
9. Torrens, F.; Castellano, G. Topological charge-transfer indices: from small molecules to proteins. Curr. Proteomics, 2009, 204 213.
10. Giuliani, A.; Di Paola, L.; Setola, R. Proteins as networks: a mesoscopic approach using haemoglobin molecule as case study. Curr. Proteomics, 2009, 6, 235-245.
11. 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. Proteomics, 2009, 6, 275-288.
12. Ivanciuc, O. Machine learning quantitative structure-activity relationships (QSAR) for peptides binding to human amphiphysin 1 SH3 domain. Curr. Proteomics, 2009, 4, 289-302.
13. Gonzalez-Diaz, H. Quantitative studies on structure-activity and structure-property relationships (QSAR/QSPR). Curr. Top. Med. Chem., 2008, 8, 1554.
14. Ivanciuc, O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr. Top. Med. Chem., 2008, 8, 1691-1709.
15. 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.
16. 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.
17. Wang, J.F.; Wei, D.Q.; Chou, K.C. Drug candidates from traditional chinese medicines. Curr. Top. Med. Chem., 2008, 8, 1656-1665.
18. Helguera, A.M.; Combes, R.D.; Gonzalez, M.P.; Cordeiro, M.N. Applications of 2D descriptors in drug design: a DRAGON tale. Curr. Top. Med. Chem., 2008, 8, 1628-1655.
19. 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.
20. 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.
21. Wang, J.F.; Wei, D.Q.; Chou, K. C. Pharmacogenomics and personalized use of drugs. Curr. Top. Med. Chem., 2008, 8, 1573 1579.
22. 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.
23. Gonzalez-Diaz, H. Network topological indices, drug metabolism, and distribution. Curr. Drug Metab., 2010, 11, 283-284.
24. Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab., 2010, 11, 369-378.
25. Garcia, I.; Diop, Y.F.; Gomez, G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr. Drug Metab., 2010, 11, 307-314.
26. 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.
27. 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.
28. 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.
29. 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.
30. Wang, J.F.; Chou, K.C. Molecular modeling of cytochrome P450 and drug metabolism. Curr. Drug Metab., 2010, 11, 342-346.
31. 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.
32. Gonzalez-Diaz, H. QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neurosciences. Curr. Pharm. Des., 2010, 16, 2598-2600.
33. 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.
34. Estrada, E.; Molina, E.; Nodarse, D.; Uriarte, E. Structural contributions of substrates to their binding to P-Glycoprotein. A TOPS-MODE approach. Curr. Pharm. Des., 2010, 16, 2676-2709.
35. Garcia, I.; Fall, Y.; Gomez, G. QSAR, docking, and CoMFA studies of GSK3 inhibitors. Curr. Pharm. Des., 2010, 16, 2666 2675.
36. Gonzalez-Diaz, H.; Romaris, F.; Duardo-Sanchez, A.; Perez Montoto, L.G.; Prado-Prado, F.; Patlewicz, G.; Ubeira, F.M. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr. Pharm. Des., 2010, 16, 2737 2764.
37. 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.
38. 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.
39. 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.
40. 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.
41. 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.
42. Prado-Prado, F.; Escobar-Cubiella, M.; García-Mera, X. Review of bioinformatics and QSAR studies of β-secretase inhibitors. Curr. Bioinformatics, 2011, 6, 3-15.
43. García, I.; Fall, Y.; Gómez, G. Trends in bioinformatics and chemoinformatics of vitamin D analogues and their protein targets. Curr. Bioinformatics, 2011, 6, 16-24.
44. Ivanciuc, T.; Ivanciuc, O.; Klein, D.J. Network-QSAR with reaction poset quantitative superstructure-activity relationships (QSSAR) for PCB chromatographic properties. Curr. Bioinformatics, 2011, 6, 25-34.
45. Chiş, O.; Dumitru, O.; Concu, R.; Shen, B. Reviewing Yeast network and report of new stochastic-credibility cell cycle models. Curr. Bioinformatics, 2011, 6, 35-43.
46. Bhattacharjee, B.; Jayadeepa, R.M.; Talambedu, U.; Banerjee, S.; Joshi, J.; Mole, J.P.; Samuel, J.; Middha, S.K. Complex network and gene ontology in pharmacology approaches: mapping natural compounds on potential drug target colon cancer network. Curr. Bioinformatics, 2011, 6, 44-52.
47. Duardo-Sanchez, A.; Patlewicz, G.; González-Díaz, H. Network topological indices from chem-bioinformatics to legal sciences and back. Curr. Bioinformatics, 2011, 6, 53-70.
48. 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. Bioinformatics, 2011, 6, 71-80.
49. 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. Bioinformatics, 2011, 6, 81-93.
50. Riera-Fernández, P.; Munteanu, C.R.; Pedreira-Souto, N.; Martín Romalde, R.; Duardo-Sanchez, A.; González-Díaz, H. Definition of Markov-Harary invariants and review of classic topological indices and databases in biology, parasitology, technology, and social-legal networks. Curr. Bioinformatics, 2011; 6: 94-121.
51. Dave, K.; Banerjee, A. Bioinformatics analysis of functional relations between CNPs regions. Curr. Bioinformatics, 2011; 6: 122-128.
52. Hajduk, P.J.; Greer, J. A decade of fragment-based drug design: strategic advances and lessons learned. Nat. Rev. Drug Discov., 2007, 6, 211-219.
53. Fattori, D.; Squarcia, A.; Bartoli, S. Fragment-based approach to drug lead discovery: overview and advances in various techniques. Drugs RD, 2008, 9, 217-227.
54. Estrada, E.; Uriarte, E.; Molina, E.; Simon-Manso, Y.; Milne, G.W. An integrated in silico analysis of drug-binding to human serum albumin. J. Chem. Inf. Model., 2006, 46, 2709-2724.
55. Tanaka, N.; Ohno, K.; Niimi, T.; Moritomo, A.; Mori, K.; Orita, M. Small-world phenomena in chemical library networks: application to fragment-based drug discovery. J. Chem. Inf. Model., 2009, 49, 2677-2686.
56. Hopkins, A.L.; Groom, C.R. The druggable genome. Nat. Rev. Drug Discov., 2002, 1, 727-730.
57. Imming, P.; Sinning, C.; Meyer, A. Drugs, their targets and the nature and number of drug targets. Nat. Rev. Drug Discov., 2006, 5, 821-834.
58. Overington, J.P.; Al-Lazikani, B.; Hopkins, A.L. How many drug targets are there? Nat. Rev. Drug Discov., 2006, 5, 993-996.
59. Yildirim, M.A.; Goh, K.I.; Cusick, M.E.; Barabasi, A.L.; Vidal, M. Drug-target network. Nature Biotechnol., 2007, 25, 1119-1126.
60. Mestres, J.; Gregori-Puigjane, E.; Valverde, S.; Sole, R.V. The topology of drug-target interaction networks: implicit dependence on drug properties and target families. Mol. Biosyst., 2009, 5, 1051 1057.
61. Csermely, P.; Agoston, V.; Pongor, S. The efficiency of multitarget drugs: the network approach might help drug design. Pharmacol. Sci., 2005, 26, 178-182.
62. Janga, S.C.; Tzakos, A. Structure and organization of drug-target networks: insights from genomic approaches for drug discovery. Mol. Biosyst., 2009, 5, 1536-1548.
63. Campillos, M.; Kuhn, M.; Gavin, A.C.; Jensen, L.J.; Bork, P. Drug target identification using side-effect similarity. Science, 2008, 321, 263-266.
64. Yamanishi, Y.; Araki, M.; Gutteridge, A.; Honda, W.; Kanehisa, M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 2008, 24, 232-240.
65. Vina, D.; Uriarte, E.; Orallo, F.; Gonzalez-Diaz, H. Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors. Mol. Pharmacol., 2009, 6, 825-835.
66. Bader, D.A.; Madduri, K. A graph-theoretic analysis of the human protein-interaction network using multicore parallel algorithms. Parallel Comput., 2008, 34, 627-639.
67. Blayney, J.K.; Zheng, H.; Wang, H.; Azuaje, F. Multi-level integrative analysis of Protein Protein Interaction networks: connecting completeness, depth and robustness. Int. J. Comput. Biol. Drug Des., 2010, 3, 31-51.
68. Yook, S.H.; Oltvai, Z.N.; Barabasi, A.L. Functional and topological characterization of protein interaction networks. Proteomics, 2004, 4, 928-942.
69. Spirin, V.; Mirny, L.A. Protein complexes and functional modules in molecular networks. Proc. Natl. Acad. Sci. USA, 2003, 100, 12123-12128.
70. Jeong, H.; Mason, S.P.; Barabasi, A.L.; Oltvai, Z.N. Lethality and centrality in protein networks. Nature, 2001, 411, 41-42.
71. Rain, J.C.; Selig, L.; De Reuse, H.; Battaglia, V.; Reverdy, C.; Simon, S.; Lenzen, G.; Petel, F.; Wojcik, J.; Schachter, V.; Chemama, Y.; Labigne, A.; Legrain, P. The protein-protein interaction map of Helicobacter pylori. Nature, 2001, 409, 211 215.
72. Hase, T.; Tanaka, H.; Suzuki, Y.; Nakagawa, S.; Kitano, H. Structure of protein interaction networks and their implications on drug design. PLoS Comput. Biol., 2009, 5, e1000550.
73. Sharan, R.; Suthram, S.; Kelley, R.M.; Kuhn, T.; McCuine, S.; Uetz, P.; Sittler, T.; Karp, R.M.; Ideker, T. Conserved patterns of protein interaction in multiple species. Proc. Natl. Acad. Sci. USA, 2005, 102, 1974-1979.
74. Nurse, P. Universal control mechanism regulating onset of Mphase. Nature, 1990, 344, 503-508.
75. Csikasz-Nagy, A.; Battogtokh, D.; Chen, K.C.; Novak, B.; Tyson, J.J. Analysis of a generic model of eukaryotic cell-cycle regulation. Biophys. J., 2006, 90, 4361-4379.
76. Nandy, S.K.; Jouhten, P.; Nielsen, J. Reconstruction of the yeast protein-protein interaction network involved in nutrient sensing and global metabolic regulation. BMC Syst. Biol., 2010, 4, 68.
77. Breitkreutz, A.; Choi, H.; Sharom, J.R.; Boucher, L.; Neduva, V.; Larsen, B.; Lin, Z.Y.; Breitkreutz, B.J.; Stark, C.; Liu, G.; Ahn, J.; Dewar-Darch, D.; Reguly, T.; Tang, X.; Almeida, R.; Qin, Z.S.; Pawson, T.; Gingras, A.C.; Nesvizhskii, A.I.; Tyers, M. A global protein kinase and phosphatase interaction network in yeast. Science, 2010, 328, 1043-1046.
78. Shi, M.G.; Huang, D.S.; Li, X.L. A protein interaction network analysis for yeast integral membrane protein. Protein Pept. Lett., 2008, 15, 692-699.
79. Lin, Y.Y.; Qi, Y.; Lu, J.Y.; Pan, X.; Yuan, D.S.; Zhao, Y.; Bader, J.S.; Boeke, J.D. A comprehensive synthetic genetic interaction network governing yeast histone acetylation and deacetylation. Genes Develop., 2008, 22, 2062-2074.
80. Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z.N.; Barabasi, A.L. The large-scale organization of metabolic networks. Nature, 2000, 407, 651-654.
81. Guimera, R.; Nunes Amaral, L.A. Functional cartography of complex metabolic networks. Nature, 2005, 433, 895-900.
82. Wagner, A.; Fell, D.A. The small world inside large metabolic networks. Proc. Biol. Sci., 2001, 268, 1803-1810.
83. Duarte, N.C.; Becker, S.A.; Jamshidi, N.; Thiele, I.; Mo, M.L.; Vo, T.D.; Srivas, R.; Palsson, B.O. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA, 2007, 104, 1777-1782.
84. Patil, K.R.; Nielsen, J. Uncovering transcriptional regulation of metabolism by using metabolic network topology. Proc. Natl. Acad. Sci. USA, 2005, 102, 2685-2689.
85. Rahman, S.A.; Schomburg, D. Observing local and global properties of metabolic pathways: 'load points' and 'choke points' in the metabolic networks. Bioinformatics, 2006, 22, 1767-1774.
86. Lemke, N.; Heredia, F.; Barcellos, C.K.; Dos Reis, A.N.; Mombach, J.C. Essentiality and damage in metabolic networks. Bioinformatics, 2004, 20, 115-119.
87. Barabasi, A.L. Network medicine--from obesity to the "diseasome". N Engl. J. Med., 2007, 357, 404-407.
88. Loscalzo, J.; Kohane, I.; Barabasi, A.L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology. Mol. Syst. Biol., 2007, 3, 124.
89. Hidalgo, C.A.; Blumm, N.; Barabasi, A.L.; Christakis, N.A. A dynamic network approach for the study of human phenotypes. PLoS Comput. Biol., 2009, 5, e1000353.
90. Goh, K.I.; Cusick, M.E.; Valle, D.; Childs, B.; Vidal, M.; Barabasi, A.L. The human disease network. Proc. Natl. Acad. Sci. USA, 2007, 104, 8685-8690.
91. Ideker, T.; Sharan, R. Protein networks in disease. Genome Res., 2008, 18, 644-652.
92. Sam, L.; Liu, Y.; Li, J.; Friedman, C.; Lussier, Y.A. Discovery of protein interaction networks shared by diseases. Pac. Symp. Biocomput., 2007, 76-87.
93. Liu, M.; Liberzon, A.; Kong, S.W.; Lai, W.R.; Park, P.J.; Kohane, I.S.; Kasif, S. Network-based analysis of affected biological processes in type 2 diabetes models. PLoS Genet., 2007, 3, e96.
94. Lee, D.S.; Park, J.; Kay, K.A.; Christakis, N.A.; Oltvai, Z.N.; Barabasi, A.L. The implications of human metabolic network topology for disease comorbidity. Proc. Natl. Acad. Sci. USA, 2008, 105, 9880-9885.
95. Xu, J.; Li, Y. Discovering disease-genes by topological features in human protein-protein interaction network. Bioinformatics, 2006, 22, 2800-2805.
96. Jonsson, P.F.; Bates, P.A. Global topological features of cancer proteins in the human interactome. Bioinformatics, 2006, 22, 2291 2297.
97. Goni, J.; Esteban, F.J.; de Mendizabal, N.V.; Sepulcre, J.; Ardanza Trevijano, S.; Agirrezabal, I.; Villoslada, P. A computational analysis of protein-protein interaction networks in neurodegenerative diseases. BMC Syst. Biol., 2008, 2, 52.
98. Hu, G.; Agarwal, P. Human disease-drug network based on genomic expression profiles. PLoS ONE, 2009, 4, e6536.
99. Prado-Prado, F.J.; Gonzalez-Diaz, H.; de la Vega, O.M.; Ubeira, F.M.; Chou, K.C. Unified QSAR approach to antimicrobials. Part 3: first multi-tasking QSAR model for input-coded prediction, structural back-projection, and complex networks clustering of antiprotozoal compounds. Bioorg. Med. Chem., 2008, 16, 5871 5880.
100. Prado-Prado, F.J.; de la Vega, O.M.; Uriarte, E.; Ubeira, F.M.; Chou, K.C.; Gonzalez-Diaz, H. Unified QSAR approach to antimicrobials. 4. Multi-target QSAR modeling and comparative multi-distance study of the giant components of antiviral drug-drug complex networks. Bioorg. Med. Chem., 2009, 17, 569-575.
101. Shirley, M.D.F.; Rushton, S.P. The impacts of network topology on disease spread. Ecol. Complex, 2005, 2, 287-299.
102. Bansal, S.; Grenfell, B.T.; Meyers, L.A. When individual behaviour matters: homogeneous and network models in epidemiology. J. R. Soc. Interface, 2007, 4, 879-891.
103. Read, J.M.; Eames, K.T.; Edmunds, W.J. Dynamic social networks and the implications for the spread of infectious disease. J. R. Soc. Interface, 2008, 5, 1001-1007.
104. Shirley, M.D.; Rushton, S.P. Where diseases and networks collide: lessons to be learnt from a study of the 2001 foot-and-mouth disease epidemic. Epidemiol. Infect., 2005, 133, 1023-1032.
105. Colizza, V.; Barrat, A.; Barthelemy, M.; Vespignani, A. The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Natl. Acad. Sci. USA, 2006, 103, 2015-2020.
106. González-Díaz, H.; Mezo, M.; González-Warleta, M.; Muíño-Pose, L.; Paniagua, E.; Ubeira, F.M. Network prediction of fasciolosis spreading in Galicia (NW Spain). In Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks, González-Díaz, H.; Munteanu, C.R., Eds. Transworld Research Network: Kerala (India), 2010; pp 191-204.
107. Menke, R. Linked: The new science of networks. Perspect. Biol. Med., 2004, 47, 300-303.
108. Huynen, M.A.; Snel, B.; von Mering, C.; Bork, P. Function prediction and protein networks. Curr. Opin. Cell Biol., 2003, 15, 191-198.
109. Chou, K.C.; Forsen, S. Graphical rules for enzyme-catalyzed rate laws. Biochem. J., 1980, 187, 829-835.
110. Zhou, G.P.; Deng, M.H. An extension of Chou's graphical rules for deriving enzyme kinetic equations to system involving parallel reaction pathways. Biochem. J., 1984, 222, 169-176.
111. Chou, K.C. Graphical rules in steady and non-steady enzyme kinetics J. Biol. Chem., 1989, 264, 12074-12079.
112. Chou, K.C. Review: applications of graph theory to enzyme kinetics and protein folding kinetics. Steady and non-steady state systems. Biophys. Chem., 1990, 35, 1-24.
113. Chou, K.C.; Zhang, C.T. Diagrammatization of codon usage in 339 HIV proteins and its biological implication. AIDS Res. Hum. Retroviruses, 1992, 8, 1967-1976.
114. Zhang, C.T.; Chou, K.C. Analysis of codon usage in 1562 E. Coli protein coding sequences. J. Mol. Biol., 1994, 238, 1-8.
115. Althaus, I.W.; Gonzales, A.J.; Chou, J.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. The quinoline U-78036 is a potent inhibitor of HIV-1 reverse transcriptase. J. Biol. Chem., 1993, 268, 14875-14880.
116. Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-88204E. Biochemistry, 1993, 32, 6548 6554.
117. Althaus, I.W.; Chou, J.J.; Gonzales, A.J.; Diebel, M.R.; Chou, K.C.; Kezdy, F.J.; Romero, D.L.; Aristoff, P.A.; Tarpley, W.G.; Reusser, F. Steady-state kinetic studies with the non-nucleoside HIV-1 reverse transcriptase inhibitor U-87201E. J. Biol. Chem., 1993, 268, 6119-6124.
118. Chou, K.C.; Kezdy, F.J.; Reusser, F. Review: Steady-state inhibition kinetics of processive nucleic acid polymerases and nucleases. Anal. Biochem., 1994, 221, 217-230.
119. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors, Wiley-VCH: 2002.
120. González-Díaz, H.; Vilar, S.; Santana, L.; Uriarte, E. Medicinal chemistry and bioinformatics current trends in drugs discovery with networks topological indices. Curr. Top. Med. Chem., 2007, 7, 1025-1039.
121. Devillers, J.; Balaban, A.T. Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach: The Netherlands, 1999.
122. Estrada, E.; Uriarte, E. Recent advances on the role of topological indices in drug discovery research. Curr. Med. Chem., 2001, 8, 1573-1588.
123. Selassie, C.D.; Mekapati, S.B.; Verma, R.P. QSAR: then and now. Curr. Top. Med. Chem., 2002, 2, 1357-1379.
124. Gozalbes, R.; Doucet, J.P.; Derouin, F. Application of topological descriptors in QSAR and drug design: history and new trends. Curr. Drug Targets Infect. Disord., 2002, 2, 93-102.
125. Honey, C.J.; Kotter, R.; Breakspear, M.; Sporns, O. Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc. Natl. Acad. Sci. USA, 2007, 104, 10240 10245.
126. McDonald, D.B. Predicting fate from early connectivity in a social network. Proc. Natl. Acad. Sci. USA, 2007, 104, 10910-10914.
127. Hahn, M.W.; Kern, A.D. Comparative genomics of centrality and essentiality in three eukaryotic protein-interaction networks. Mol. Biol. Evol., 2005, 22, 803-806.
128. Altmann, M. Reinterpreting network measures for models of disease transmission. Soc. Networks, 1993, 15, 1-17.
129. Bonchev, D.; Buck, G.A. From molecular to biological structure and back. J. Chem. Inf. Model., 2007, 47, 909-917.
130. Boccaletti, S.; Latora, V.; Moreno, Y.; Chavez, M.; Hwang, D.U. Complex networks: structure and dynamics. Phys. Rep., 2006, 424, 175-308.
131. Balaban, A.T.; Basak, S.C.; Beteringhe, A.; Mills, D.; Supuran, C.T. QSAR study using topological indices for inhibition of carbonic anhydrase II by sulfanilamides and Schiff bases. Mol. Divers., 2004, 8, 401-412.
132. Abraham, M.H.; Kumarsingh, R.; Cometto-Muniz, J.E.; Cain, W.S. Draize eye scores and eye irritation thresholds in man can be combined into one QSAR. Ann. N Y Acad. Sci., 1998, 855, 652 656.
133. Agrawal, V.K.; Bano, S.; Khadikar, P.V. Topological approach to quantifying molecular lipophilicity of heterogeneous set of organic compounds. Bioorg. Med. Chem., 2003, 11, 4039-4047.
134. Barratt, M.D. Integrating computer prediction systems with in vitro methods towards a better understanding of toxicology. Toxicol. Lett., 1998, 102-103, 617-621.
135. Benigni, R.; Andreoli, C.; Giuliani, A. QSAR models for both mutagenic potency and activity: application to nitroarenes and aromatic amines. Environ. Mol. Mutagen., 1994, 24, 208-219.
136. Garcia-Garcia, A.; Galvez, J.; de Julian-Ortiz, J.V.; Garcia Domenech, R.; Munoz, C.; Guna, R.; Borras, R. Search of chemical scaffolds for novel antituberculosis agents. J. Biomol. Screen., 2005, 10, 206-214.
137. Katritzky, A.R.; Gordeeva, E.V. Traditional topological indices vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research. J. Chem. Inf. Comput. Sci., 1993, 33, 835 857.
138. Estrada, E. Generalization of topological indices. Chem. Phys. Lett., 2001, 336, 248-252.
139. Gonzalez-Diaz, H.; Cruz-Monteagudo, M.; Vina, D.; Santana, L.; Uriarte, E.; De Clercq, E. QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorg. Med. Chem. Lett., 2005, 15, 1651-1657.
140. Matamala, A.R.; Estrada, E. Simplex optimization of generalized topological index (GTI-Simplex): a unified approach to optimize QSPR models. J. Phys. Chem. A, 2005, 109, 9890-9895.
141. Matamala, A.R.; Estrada, E. Generalised topological indices: Optimisation methodology and physico-chemical interpretation. Chem. Phys. Lett., 2005, 410, 343-347.
142. González-Díaz, H.; Cruz-Monteagudo, M.; Vina, D.; Santana, L.; Uriarte, E.; De Clercq, E. QSAR for anti-RNA-virus activity, synthesis, and assay of anti-RSV carbonucleosides given a unified representation of spectral moments, quadratic, and topologic indices. Bioorg. Med. Chem. Lett., 2005, 15, 1651-1657.
143. Marrero-Ponce, Y.; Medina-Marrero, R.; Torrens, F.; Martinez, Y.; Romero-Zaldivar, V.; Castro, E.A. Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. Bioorg. Med. Chem., 2005, 13, 2881-2899.
144. Marrero-Ponce, Y.; Montero-Torres, A.; Zaldivar, C.R.; Veitia, M.I.; Perez, M.M.; Sanchez, R.N. Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds. Bioorg. Med. Chem., 2005, 13, 1293 1304.
145. Sabidussi, G. The Centrality Index of a Graph. Psychometrika. 1966, 31, 581-603.
146. Freeman, L.C. A set of measures of centrality based upon betweenness. Sociometry, 1977, 40, 35-41.
147. Freeman LC. Centrality in social networks conceptual clarification. Social networks 1979; 1: 215-39.
148. Borgatti SP, Everett MG. A Graph-theoretic perspective on centrality. Social Networks 2006; 28: 466-484.
149. Junker, B.H.; Koschuetzki, D.; Schreiber, F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics, 2006, 7, 219.
150. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13, 2498-2504.
151. Cline, M.S.; Smoot, M.; Cerami, E.; Kuchinsky, A.; Landys, N.; Workman, C.; Christmas, R.; Avila-Campilo, I.; Creech, M.; Gross, B.; Hanspers, K.; Isserlin, R.; Kelley, R.; Killcoyne, S.; Lotia, S.; Maere, S.; Morris, J.; Ono, K.; Pavlovic, V.; Pico, A.R.; Vailaya, A.; Wang, P.L.; Adler, A.; Conklin, B.R.; Hood, L.; Kuiper, M.; Sander, C.; Schmulevich, I.; Schwikowski, B.; Warner, G.J.; Ideker, T.; Bader, G.D. Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc., 2007, 2, 2366-2382.
152. Smoot, M.E.; Ono, K.; Ruscheinski, J.; Wang, P.L.; Ideker, T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 2011, 27, 431-432.
153. Estrada, E.; Rodriguez-Velazquez, J.A. Subgraph centrality in complex networks. Phys. Rev. E, 2005, 71(5 Pt 2), 056103.
154. Aguero-Chapin, G.; Antunes, A.; Ubeira, F.M.; Chou, K.C.; Gonzalez-Diaz, H. Comparative study of topological indices of macro/supramolecular RNA complex networks. J. Chem. Inf. Model., 2008, 48, 2265-2277.
155. Estrada, E. Virtual identification of essential proteins within the protein interaction network of yeast. Proteomics, 2006, 6, 35-40.
156. Fung, D.C. Modeling and analyzing gene co-expression in hepatocellular carcinoma using actor-semiotic networks and centrality signatures. Cancer Inform., 2008, 6, 463-474.
157. Bassett, D.S.; Bullmore, E.; Verchinski, B.A.; Mattay, V.S.; Weinberger, D.R.; Meyer-Lindenberg, A. Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci., 2008, 28, 9239-9248.
158. Berca, M.N.; Duardo-Sanchez, A.; González-Díaz, H.; Pazos, A.; Munteanu, C.R. Markov entropy for biology, parasitology, linguistic, technology, social and law networks. In Complex Network Entropy: From Molecules to Biology, Parasitology, Technology, Social, Legal, and Neurosciences, González-Díaz, H.; Prado-Prado, F. J.; García-Mera, X., Eds. Transworld Research Network: Kerala, India, 2011; pp 127-142.
159. Aguiar-Pulido, V.; Seoane-Fernández, J.A.; Freire-Veiga, A.M.; Gonzalez-Diaz, H.; Duardo-Sanchez, A.; Dorado, J.; Pazos, A.; Munteanu, C.R. New Markov-Randic centralities for computational methods of biology, parasitology, technology, social and law networks. ICCMSE 2010; Kos (Greece).
160. Shannon, C.E. A mathematical theory of communication. Bell Syst. Tech. J., 1948, 27, 379-423.
161. Randić, M. Characterization of molecular branching. J. Am. Chem. Soc., 1975, 97, 6609-6615.
162. Agrawal, V.K.; Banerji, M.; Gupta, M.; Singh, J.; Khadikar, P.V.; Supuran, C.T. QSAR study on carbonic anhydrase inhibitors: water-soluble sulfonamides incorporating beta-alanyl moieties, possessing long lasting-intra ocular pressure lowering properties--a molecular connectivity approach. Eur. J. Med. Chem., 2005, 40, 1002-1012.
163. Smissaert, H.R.; Jansen, A.A. On the variation of toxic effects over species, its cause, and analysis by "structure-selectivity relations". Ecotoxicol. Environ. Saf., 1984, 8, 294-302.
164. Bermudez, C.I.; Daza, E.E.; Andrade, E. Characterization and comparison of Escherichia coli transfer RNAs by graph theory based on secondary structure. J. Theor. Biol., 1999, 197, 193-205.
165. Prabhakar, Y.S.; Rawal, R.K.; Gupta, M.K.; Solomon, V.R.; Katti, S.B. Topological descriptors in modeling the HIV inhibitory activity of 2-aryl-3-pyridyl-thiazolidin-4-ones. Comb. Chem. High Throughput Screen., 2005, 8, 431-437.
166. Fernandez, M.; Caballero, J.; Helguera, A.M.; Castro, E.A.; Gonzalez, M.P. Quantitative structure-activity relationship to predict differential inhibition of aldose reductase by flavonoid compounds. Bioorg. Med. Chem., 2005, 13, 3269-3277.
167. Garcia-Domenech, R.; Galvez, J.; de Julian-Ortiz, J.V.; Pogliani, L. Some new trends in chemical graph theory. Chem. Rev., 2008, 108, 1127-1169.
168. Gálvez, J.; García, R.; Salabert, M.T.; Soler, R. Charge indexes. New topological descriptors. J. Chem. Inf. Comput. Sci., 1994, 34, 520-525.
169. Gonzalez Diaz, H.; Olazabal, E.; Castanedo, N.; Sanchez, I.H.; Morales, A.; Serrano, H.S.; Gonzalez, J.; de Armas, R.R. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides. J. Mol. Model., 2002, 8, 237-245.
170. Gonzalez-Diaz, H.; Torres-Gomez, L.A.; Guevara, Y.; Almeida, M.S.; Molina, R.; Castanedo, N.; Santana, L.; Uriarte, E. Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer-aided molecular design III: 2.5D indices for the discovery of antibacterials. J. Mol. Model., 2005, 11, 116-123.
171. Estrada, E.; Bodin, O. Using network centrality measures to manage landscape connectivity. Ecol. Appl., 2008, 18, 1810-1825.