Predicting drugs and proteins in parasite infections with topological indices of complex networks: Theoretical backgrounds, applications and legal issues

Current Pharmaceutical Design

Volumn 16, Issue 24, 2010, Pages 2737-2764

  González Díaz, Humberto ^a   Romaris, Fernanda ^a   Duardo Sanchez, Aliuska ^a   Pérez Montoto, Lázaro G ^a   Prado Prado, Francisco ^a   Patlewicz, Grace ^b,c   Ubeira, Florencio M ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

^b JOINT RESEARCH CENTRE   (Italy)

^c DUPONT COMPANY   (United States)

Author keywords

Anti protozooal agents; Complex networks; Fasciolosis; Graph theory; Lesihmaniosis Leishmaniasis; Malaria; Markov Chains; QSAR; Topological Indices; Trypanosomiasis

Indexed keywords

3,5,5 TRIMETHYLOXAZOLIDINE 2,4 DIONE; ANTIMALARIAL AGENT; ANTIPARASITIC AGENT; ANTIPROTOZOAL AGENT; GATIFLOXACIN; GREPAFLOXACIN; GUANABENZ ACETATE; MOXIFLOXACIN; QUINOLONE DERIVATIVE; SULFAMETHOXAZOLE; THIOACETAZONE; TROVAFLOXACIN; UNCLASSIFIED DRUG;

ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; DRUG SCREENING; DRUG TARGETING; HIDDEN MARKOV MODEL; LEGAL ASPECT; PARASITOSIS; PREDICTION; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; QUANTITATIVE STRUCTURE ACTIVITY RELATION;

ANIMALS; ANTIPARASITIC AGENTS; COMPUTER SIMULATION; DATABASES, FACTUAL; DATABASES, PROTEIN; DRUG DESIGN; HUMANS; MARKOV CHAINS; MODELS, MOLECULAR; MOLECULAR STRUCTURE; MOLECULAR TARGETED THERAPY; PARASITIC DISEASES; PROTEIN INTERACTION MAPPING; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP; STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 77957956659     PISSN: 13816128     EISSN: None     Source Type: Journal    
DOI: 10.2174/138161210792389234     Document Type: Article

References (133)

1. Hotez PJ, Kamath A. Neglected tropical diseases in sub-saharan Africa: review of their prevalence, distribution, and disease burden. PLoS Negl Trop Dis 2009; 3: e412.
2. Franco-Paredes C, Bottazzi ME, Hotez PJ. The unfinished public health agenda of chagas disease in the era of globalization. PLoS Negl Trop Dis 2009; 3: e470.
3. Hotez PJ. One world health: neglected tropical diseases in a flat world. PLoS Negl Trop Dis 2009; 3: e405.
4. Hotez PJ, Wilkins PP. Toxocariasis: america's most common neglected infection of poverty and a helminthiasis of global importance? PLoS Negl Trop Dis 2009; 3: e400.
5. Utzinger J, Xiao SH, Tanner M, Keiser J. Artemisinins for schistosomiasis and beyond. Curr Opin Investig Drugs 2007; 8: 105-16.
6. Matthys B, Tschannen AB, Tian-Bi NT, et al. Risk factors for Schistosoma mansoni and hookworm in urban farming communities in western Cote d'Ivoire. Trop Med Int Health 2007; 12: 709-23.
7. Mukhopadhyay A, Peterson RT. Fishing for new antimicrobials. Curr Opin Chem Biol 2006; 10: 327-33.
8. Balaban AT, Basak SC, Beteringhe A, Mills D, Supuran CT. QSAR study using topological indices for inhibition of carbonic anhydrase II by sulfanilamides and Schiff bases. Mol Divers 2004; 8: 401-12.
9. Abraham MH, Kumarsingh R, Cometto-Muniz JE, Cain WS. Draize eye scores and eye irritation thresholds in man can be combined into one QSAR. Ann N Y Acad Sci 1998; 855: 652-6.
10. Agrawal VK, Bano S, Khadikar PV. Topological approach to quantifying molecular lipophilicity of heterogeneous set of organic compounds. Bioorg Med Chem 2003; 11: 4039-47.
11. Barratt MD. Integrating computer prediction systems with in vitro methods towards a better understanding of toxicology. Toxicol Lett 1998; 102-103: 617-21.
12. 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: 208-19.
13. Garcia-Garcia A, Galvez J, de Julian-Ortiz JV, et al. Search of chemical scaffolds for novel antituberculosis agents. J Biomol Screen 2005; 10: 206-14.
14. Katritzky AR, Gordeeva EV. Traditional topological indices vs electronic, geometrical, and combined molecular descriptors in QSAR/QSPR research. J Chem Inf Comput Sci 1993; 33: 835-57.
15. González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008; 8: 750-78.
16. 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: 1025-39.
17. Estrada E, Uriarte E. Recent advances on the role of topological indices in drug discovery research. Curr Med Chem 2001; 8: 1573-88.
18. Garcia-Domenech R, Galvez J, de Julian-Ortiz JV, Pogliani L. Some new trends in chemical graph theory. Chem Rev 2008; 108: 1127-69.
19. Todeschini R, Consonni V. Handbook of molecular descriptors. USA: Wiley-VCH 2002.
20. Estrada E. Generalization of topological indices. Chem Phys Lett 2001; 336: 248-52.
21. 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: 1651-7.
22. Devillers J, Balaban AT. Topological Indices and Related Descriptors in QSAR and QSPR. The Netherlands: Gordon and Breach 1999.
23. Matamala AR, Estrada E. Simplex optimization of generalized topological index (GTI-Simplex): a unified approach to optimize QSPR models. J Phys Chem A 2005; 109: 9890-5.
24. Matamala AR, Estrada E. Generalised topological indices: Optimisation methodology and physico-chemical interpretation. Chem Phys Lett 2005; 410 343-47.
25. 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: 1651-7.
26. Hill T, Lewicki P. Statistics methods and applications. A comprehensive reference for science, industry and data mining. Tulsa: StatSoft 2006.
27. Karelson M, Lobanov VS, Katritzky AR. Quantum-chemical descriptors in QSAR/QSPR Studies. Chem Rev 1996; 96: 1027-44.
28. Katritzky AR, Kulshyn OV, Stoyanova-Slavova I, et al. Antimalarial activity: a QSAR modeling using CODESSA PRO software. Bioorg Med Chem 2006; 14: 2333-57.
29. Katritzky AR, Dobchev DA, Tulp I, Karelson M, Carlson DA. QSAR study of mosquito repellents using Codessa Pro. Bioorg Med Chem Lett 2006; 16: 2306-11.
30. Estrada E, Diaz GA, Delgado EJ. Predicting infinite dilution activity coefficients of organic compounds in water by quantum-connectivity descriptors. J Comput Aided Mol Des 2006; 20: 539-48.
31. Estrada E, Uriarte E. Quantitative structure--toxicity relationships using TOPS-MODE. 1. Nitrobenzene toxicity to Tetrahymena pyriformis. SAR QSAR Environ Res 2001; 12: 309-24.
32. Gao H, Bajorath J. Comparison of binary and 2D QSAR analyses using inhibitors of human carbonic anhydrase II as a test case. Mol Divers 1998; 4: 115-30.
33. Gupta MK, Prabhakar YS. QSAR study on tetrahydroquinoline analogues as plasmodium protein farnesyltransferase inhibitors: a comparison of rationales of malarial and mammalian enzyme inhibitory activities for selectivity. Eur J Med Chem 2008; 43: 2751-67.
34. Helguera AM, Combes RD, Gonzalez MP, Cordeiro MN. Applications of 2D descriptors in drug design: a DRAGON tale. Curr Top Med Chem 2008; 8: 1628-55.
35. 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: 1555-72.
36. Hoffman BT, Kopajtic T, Katz JL, Newman AH. 2D QSAR modeling and preliminary database searching for dopamine transporter inhibitors using genetic algorithm variable selection of Molconn Z descriptors. J Med Chem 2000; 43: 4151-9.
37. Tetko IV, Gasteiger J, Todeschini R, et al. Virtual computational chemistry laboratory--design and description. J Comput Aided Mol Des 2005; 19: 453-63.
38. Gozalbes R, Galvez J, Moreno A, Garcia-Domenech R. Discovery of new antimalarial compounds by use of molecular connectivity techniques. J Pharm Pharmacol 1999; 51: 111-7.
39. Mahmoudi N, de Julian-Ortiz JV, Ciceron L, et al. Identification of new antimalarial drugs by linear discriminant analysis and topological virtual screening. J Antimicrob Chemother 2006; 57: 489-97.
40. Mahmoudi N, Garcia-Domenech R, Galvez J, et al. New active drugs against liver stages of Plasmodium predicted by molecular topology. Antimicrob Agents Chemother 2008; 52: 1215-20.
41. Armstrong D, Gold JW, Dryjanski J, et al. Treatment of infections in patients with the acquired immunodeficiency syndrome. Ann Intern Med 1985; 103: 738-43.
42. Gozalbes R, Galvez J, Garcia-Domenech R, Derouin F. Molecular search of new active drugs against Toxoplasma gondii. SAR QSAR Environ Res 1999; 10: 47-60.
43. Gozalbes R, Brun-Pascaud M, Garcia-Domenech R, et al. Antitoxoplasma activities of 24 quinolones and fluoroquinolones in vitro: prediction of activity by molecular topology and virtual computational techniques. Antimicrobial Agents Chemotherapy 2000; 44: 2771-76.
44. Marrero-Ponce Y, Diaz HG, Zaldivar VR, Torrens F, Castro EA. 3D-chiral quadratic indices of the 'molecular pseudograph's atom adjacency matrix' and their application to central chirality codification: classification of ACE inhibitors and prediction of sigma-receptor antagonist activities. Bioorg Med Chem 2004; 12: 5331-42.
45. Marrero-Ponce Y, Medina-Marrero R, Torrens F, Martinez Y, Romero-Zaldivar V, Castro EA. Atom, atom-type, and total nonstochastic and stochastic quadratic fingerprints: a promising approach for modeling of antibacterial activity. Bioorg Med Chem 2005; 13: 2881-99.
46. Casanola-Martin GM, Khan MT, Marrero-Ponce Y, Ather A, Sultankhodzhaev MN, Torrens F. New tyrosinase inhibitors selected by atomic linear indices-based classification models. Bioorg Med Chem Lett 2006; 16: 324-30.
47. Marrero-Ponce Y, Torrens F, Alvarado YJ, Rotondo R. Bond-based global and local (bond, group and bond-type) quadratic indices and their applications to computer-aided molecular design. 1. QSPR studies of diverse sets of organic chemicals. J Comput Aided Mol Des 2006; 20: 685-701.
48. Alvarez-Ginarte YM, Marrero-Ponce Y, Ruiz-Garcia JA, et al. Applying pattern recognition methods plus quantum and physico-chemical molecular descriptors to analyze the anabolic activity of structurally diverse steroids. J Comput Chem 2008; 29(3): 317-33.
49. Casanola-Martin GM, Marrero-Ponce Y, Khan MT, et al. TOMOCOMD-CARDD descriptors-based virtual screening of tyrosinase inhibitors: evaluation of different classification model combinations using bond-based linear indices. Bioorg Med Chem 2007; 15: 1483-503.
50. Castillo-Garit JA, Marrero-Ponce Y, Torrens F, Rotondo R. Atom-based stochastic and non-stochastic 3D-chiral bilinear indices and their applications to central chirality codification. J Mol Graph Model 2007; 26: 32-47.
51. Marrero-Ponce Y, Castillo Garit JA, Nodarse D. Linear indices of the 'macromolecular graph's nucleotides adjacency matrix' as a promising approach for bioinformatics studies. Part 1: prediction of paromomycin's affinity constant with HIV-1 psi-RNA packaging region. Bioorg Med Chem 2005; 13: 3397-404.
52. Marrero-Ponce Y, Nodarse D, González-Díaz H, et al. Nucleic Acid Quadratic Indices of the "Macromolecular Graph's Nucleotides Adjacency Matrix". Modeling of Footprints after the Interaction of Paromomycin with the HIV-1 -RNA Packaging Region. Int J Mol Sci 2004; 5: 276-93.
53. Marrero-Ponce Y, Ortega-Broche SE, Diaz YE, et al. Nucleotide's bilinear indices: novel bio-macromolecular descriptors for bioinformatics studies of nucleic acids. I. Prediction of paromomycin's affinity constant with HIV-1 Psi-RNA packaging region. J Theor Biol 2009; 259: 229-41.
54. Marrero-Ponce Y, Medina-Marrero R, Castillo-Garit JA, Romero-Zaldivar V, Torrens F, Castro EA. Protein linear indices of the 'macromolecular pseudograph alpha-carbon atom adjacency matrix' in bioinformatics. Part 1: prediction of protein stability effects of a complete set of alanine substitutions in Arc repressor. Bioorg Med Chem 2005; 13: 3003-15.
55. Marrero-Ponce Y, Medina-Marrero R, Castro AE, et al. Protein Quadratic Indices of the "Macromolecular Pseudograph's -Carbon Atom Adjacency Matrix". 1. Prediction of Arc Repressor Alaninemutant's Stability. Molecules 2004; 9: 1124-47.
56. Castillo-Garit JA, Marrero-Ponce Y, Torrens F. Atom-based 3D-chiral quadratic indices. Part 2: Prediction of the corticosteroid-binding globulin binding affinity of the 31 benchmark steroids data set. Bioorg Med Chem 2006; 14: 2398-408.
57. Marrero-Ponce Y, Khan MT, Casanola-Martin GM, et al. Bondbased 2D TOMOCOMD-CARDD approach for drug discovery: aiding decision-making in 'in silico' selection of new lead tyrosinase inhibitors. J Comput Aided Mol Des 2007; 21: 167-88.
58. Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, et al. TOMOCOMD-CARDD, a novel approach for computer-aided 'rational' drug design: I. Theoretical and experimental assessment of a promising method for computational screening and in silico design of new anthelmintic compounds. J Comput Aided Mol Des 2004; 18: 615-34.
59. Marrero-Ponce Y, Castillo-Garit JA, Olazabal E, et al. Atom, atom-type and total molecular linear indices as a promising approach for bioorganic and medicinal chemistry: theoretical and experimental assessment of a novel method for virtual screening and rational design of new lead anthelmintic. Bioorg Med Chem 2005; 13: 1005-20.
60. Schwebke JR, Barrientes FJ. Prevalence of trichomonas vaginalis isolates with resistance to metronidazole and tinidazole. Antimicrob Agents Chemother 2006; 50: 4209-10.
61. Johnson PJ. Metronidazole and drug resistance. Parasitol Today 1993; 9: 183-6.
62. Marrero-Ponce Y, Machado-Tugores Y, Pereira DM, et al. A computer-based approach to the rational discovery of new tricho-monacidal drugs by atom-type linear indices. Curr Drug Discov Technol 2005; 2: 245-65.
63. Meneses-Marcel A, Marrero-Ponce Y, Machado-Tugores Y, et al. A linear discrimination analysis based virtual screening of tricho-monacidal lead-like compounds: outcomes of in silico studies supported by experimental results. Bioorg Med Chem Lett 2005; 15: 3838-43.
64. Marrero-Ponce Y, Meneses-Marcel A, Castillo-Garit JA, et al. Predicting antitrichomonal activity: a computational screening using atom-based bilinear indices and experimental proofs. Bioorg Med Chem 2006; 14: 6502-24.
65. Meneses-Marcel A, Rivera-Borroto OM, Marrero-Ponce Y, et al. New antitrichomonal drug-like chemicals selected by bond (edge)-based TOMOCOMD-CARDD descriptors. J Biomol Screen 2008; 13: 785-94.
66. Marrero-Ponce Y, Iyarreta-Veitia M, Montero-Torres A, et al. Ligand-based virtual screening and in silico design of new antimalarial compounds using nonstochastic and stochastic total and atom-type quadratic maps. J Chem Inf Model 2005; 45: 1082-100.
67. Marrero-Ponce Y, Montero-Torres A, Zaldivar CR, Veitia MI, Perez MM, Sanchez RN. 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: 1293-304.
68. Montero-Torres A, Garcia-Sanchez RN, Marrero-Ponce Y, et al. Non-stochastic quadratic fingerprints and LDA-based QSAR models in hit and lead generation through virtual screening: theoretical and experimental assessment of a promising method for the discovery of new antimalarial compounds. Eur J Med Chem 2006; 41: 483-93.
69. Montero-Torres A, Vega MC, Marrero-Ponce Y, et al. A novel non-stochastic quadratic fingerprints-based approach for the 'in silico' discovery of new antitrypanosomal compounds. Bioorg Med Chem 2005; 13: 6264-75.
70. Vega MC, Montero-Torres A, Marrero-Ponce Y, et al. New ligandbased approach for the discovery of antitrypanosomal compounds. Bioorg Med Chem Lett 2006; 16: 1898-904.
71. Roldos V, Nakayama H, Rolon M, et al. Activity of a hydroxy-bibenzyl bryophyte constituent against Leishmania spp. and Try-panosoma cruzi: in silico, in vitro and in vivo activity studies. Eur J Med Chem 2008; 43: 1797-807.
72. Ibarra-Velarde F, Vera-Montenegro Y, Huesca-Guillen A, Canto-Alarcon G, Alcala-Canto Y, Marrero-Ponce Y. In silico fasciolicide activity of three experimental compounds in sheep. Ann N Y Acad Sci 2008; 1149: 183-5.
73. Gonzalez-Diaz H, Prado-Prado F, Ubeira FM. Predicting antimi-crobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem 2008; 8: 1676-90.
74. Santana L, Gonzalez-Diaz H, Quezada E, et al. Quantitative structure-activity relationship and complex network approach to monoamine oxidase a and B inhibitors. J Med Chem 2008; 51: 6740-51.
75. Santana L, Uriarte E, González-Díaz H, Zagotto G, Soto-Otero R, Mendez-Alvarez E. A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biological assay of novel coumarins. J Med Chem 2006; 49: 1149-56.
76. 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 Pharm 2009; 6: 825-35.
77. Concu R, Podda G, Uriarte E, Gonzalez-Diaz H. Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J Comput Chem 2008; doi:10.1002/jcc.
78. Saiz-Urra L, González-Díaz H, Uriarte E. Proteins Markovian 3D-QSAR with spherically-truncated average electrostatic potentials. Bioorg Med Chem 2005; 13: 3641-7.
79. González-Díaz H, Saiz-Urra L, Molina R, Santana L, Uriarte E. A Model for the Recognition of Protein Kinases Based on the Entropy of 3D van der Waals Interactions. J Proteome Res 2007; 6: 904-08.
80. Gonzalez-Diaz H, Saiz-Urra L, Molina R, Gonzalez-Diaz Y, Sanchez-Gonzalez A. Computational chemistry approach to protein kinase recognition using 3D stochastic van der Waals spectral moments. J Comput Chem 2007; 28: 1042-8.
81. Gonzalez-Diaz H, Molina R, Uriarte E. Recognition of stable protein mutants with 3D stochastic average electrostatic potentials. FEBS Lett 2005; 579: 4297-301.
82. Concu R, Podda G, Uriarte E, Gonzalez-Diaz H. Computational chemistry study of 3D-structure-function relationships for enzymes based on Markov models for protein electrostatic, HINT, and van der Waals potentials. J Comput Chem 2009; doi:10.1002/jcc.
83. González-Díaz H, Pérez-Castillo Y, Podda G, Uriarte E. Computational Chemistry Comparison of Stable/Nonstable Protein Mutants Classification Models Based on 3D and Topological Indices. J Comput Chem 2007; 28: 1990-95.
84. Agüero-Chapin G, Gonzalez-Diaz H, Molina R, Varona-Santos J, Uriarte E, Gonzalez-Diaz Y. Novel 2D maps and coupling numbers for protein sequences. The first QSAR study of polygalacturonases; isolation and prediction of a novel sequence from Psidium guajava L. FEBS Lett 2006; 580: 723-30.
85. González-Díaz H, Agüero-Chapin G, Varona J, 2D-RNA-coupling numbers: a new computational chemistry approach to link secondary structuretopology with biological function. J Comput Chem 2007; 28: 1049-56.
86. González-Díaz H, Prado-Prado F, Ubeira FM. Predicting Antimicrobial Drugs and Targets with the MARCH-INSIDE approach. Cuerr Top Med Chem 2008; 8: 1676-90.
87. González-Díaz H, Olazabal E, Castanedo N, et al. 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: 237-45.
88. Mas-Coma S, Bargues MD, Valero MA. Fascioliasis and other plant-borne trematode zoonoses. Int J Parasitol 2005; 35: 1255-78.
89. Mas-Coma MS, Esteban JG, Bargues MD. Epidemiology of human fascioliasis: a review and proposed new classification. Bull World Health Organ 1999; 77: 340-6.
90. Allen PC, Fetterer RH. Recent advances in biology and immunobiology of Eimeria species and in diagnosis and control of infection with these coccidian parasites of poultry. Clin. Microbiol. Rev. 2002; 15: 58-65.
91. González-Díaz H, Bastida I, Castanedo N, et al. Simple stochastic fingerprints towards mathematical modelling in biology and medicine. 1. The treatment of coccidiosis. Bull Math Biol 2004; 66: 1285-311.
92. Randić M. Orthogonal molecular descriptors. New J Chem 1991; 15: 517-25.
93. Randić M. Resolution of ambiguities in structure-property studies by use of orthogonal descriptors. J Chem Inf Comput Sci 1991; 31: 311-70.
94. Prado-Prado FJ, Gonzalez-Diaz H, de la Vega OM, Ubeira FM, Chou KC. 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: 5871-80.
95. Prado-Prado FJ, Ubeira FM, Borges F, Gonzalez-Diaz H. Unified QSAR & network-based computational chemistry approach to antimicrobials. II. Multiple distance and triadic census analysis of antiparasitic drugs complex networks. J Comput Chem 2009; 31: 164-73.
96. Prado-Prado FJ, Garcia-Mera X, Gonzalez-Diaz H. Multi-target spectral moment QSAR versus ANN for antiparasitic drugs against different parasite species. Bioorg Med Chem 2010; 18: 2225-31.
97. Munteanu CR, Vazquez JM, Dorado J, et al. Complex network spectral moments for ATCUN motif DNA cleavage: first predictive study on proteins of human pathogen parasites. J Proteome Res 2009; 8: 5219-28.
98. Gonzalez-Diaz H, Dea-Ayuela MA, Perez-Montoto LG, et al. QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol Divers 20010; 14(2): 349-69.
99. Aguero-Chapin G, Gonzalez-Diaz H, de la Riva G, et al. MMMQSAR recognition of ribonucleases without alignment: comparison with an HMM model and isolation from Schizosaccharomyces pombe, prediction, and experimental assay of a new sequence. J Chem Inf Model 2008; 48: 434-48.
100. Dea-Ayuela MA, Perez-Castillo Y, Meneses-Marcel A, et al. HP-Lattice QSAR for dynein proteins: experimental proteomics (2Delectrophoresis, mass spectrometry) and theoretic study of a Leishmania infantum sequence. Bioorg Med Chem 2008; 16: 7770-6.
101. Rodriguez-Soca Y, Munteanu CR, Dorado J, Rabuñal J, Pazos A, González-Díaz. Plasmod-PPI: A web-server predicting complex biopolymer targets in plasmodium with entropy measures of protein-protein interactions. Polymer 2010; 51: 264-73.
102. Rodriguez-Soca Y, Munteanu CR, Dorado J, Pazos A, Prado-Prado FJ, Gonzalez-Diaz H. Trypano-PPI: a web server for prediction of unique targets in trypanosome proteome by using electrostatic parameters of protein-protein interactions. J Proteome Res 2010; 9: 1182-90.
103. Vermeirssen V, Barrasa MI, Hidalgo CA, et al. Transcription factor modularity in a gene-centered C. elegans core neuronal protein-DNA interaction network. Genome Res 2007; 17: 1061-71.
104. LaCount DJ, Vignali M, Chettier R, et al. A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 2005; 438: 103-07.
105. Borenstein E, Feldman MW. Topological signatures of species interactions in metabolic networks. J Comput Biol 2009; 16: 191-200.
106. Mezo M, Gonzalez-Warleta M, Castro-Hermida JA, Ubeira FM. Evaluation of the flukicide treatment policy for dairy cattle in Galicia (NW Spain). Vet Parasitol 2008; 157: 235-43.
107. Mas-Coma S. Epidemiology of fascioliasis in human endemic areas. J Helminthol 2005; 79: 207-16.
108. González-Díaz H, Mezo M, González-Warleta M, Muíño-Pose L, Paniagua E, Ubeira FM. Network prediction of fasciolosis spreading in Galicia. In: González-Díaz H, Munteanu CR, Eds. (NW Spain) Kerala: Transworld Research Network 2010; pp. 191-204.
109. Wollbold J, Huber R, Pohlers D, et al. Adapted Boolean network models for extracellular matrix formation. BMC Syst Biol 2009; 3: 77.
110. Pomerance A, Ott E, Girvan M, Losert W. The effect of network topology on the stability of discrete state models of genetic control. Proc Natl Acad Sci U S A 2009; 106: 8209-14.
111. Zhang SQ, Ching WK, Ng MK, Akutsu T. Simulation study in Probabilistic Boolean Network models for genetic regulatory networks. Int J Data Min Bioinform 2007; 1: 217-40.
112. Junker BH, Koschuetzki D, Schreiber F. Exploration of biological network centralities with CentiBiN. BMC Bioinformatics 2006; 7: 219.
113. StatSoft.Inc., 2002.
114. Zhang W. Computer inference of network of ecological interactions from sampling data. Environ Monit Assess 2007; 124: 253-61.
115. da Silveira CH, Pires DE, Minardi RC, et al. Protein cutoff scanning: A comparative analysis of cutoff dependent and cutoff free methods for prospecting contacts in proteins. Proteins 2009; 74: 727-43.
116. Forst CV. Host-pathogen systems biology. DDT 2006; 11: 220-7.
117. Munteanu CR, Dorado J, Pazos Sierra A, et al. In: E-sF Dehmer M, Mehler A, Ed. Markov entropy centrality: chemical, biological, crime and legislative networks. USA: Springer-Verlag 2010.
118. Estrada E, Molina E. 3D connectivity indices in QSPR/QSAR studies. J Chem Inf Comput Sci 2001; 41: 791-7.
119. 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: 1666-75.
120. Fenwick & West LLP: San Francisco 2007; Vol. 2007.
121. González-Díaz H, Prado-Prado F, Pérez-Montoto LG, 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: 214-27.
122. González-Díaz H, Agüero-Chapin G, Munteanu CR, et al. In: Osborne MA, Ed. Alignment-free models in plant genomics: theoretical, experimental, and legal issues. New York: Nova Sciences 2010; Vol. 1, pp.
123. González-Díaz H, Duardo-Sanchez A, Ubeira FM, et al. Review of MARCH-INSIDE & Complex networks prediction of drugs: AD-MET, anti-parasite activity, metabolizing enzymes and cardiotoxicity proteome biomarkers. Curr Drug Metabol 2010; 11: 379-406.
124. Steering committee for intellectual property issues in software computer science and telecommunications board commission on physical sciences M, and Applications national research council. Intellectual Property Issues In software, Washington, D.C: National Academy Press 1991.
125. Westkamp GN. Protección del material biológico mediante derechos de autor. ¿Vuelta de la Bioinformática a los Derechos de autor en la biotecnología? IPR-Helpdesk Bulletin 2005.
126. Story A. In: ICTSD-UNCTAD, Ed. Intellectual Property and Computer Software. Chavanod: Imprimerie Typhon 2004; Vol. 12.
127. Morcon C, Roughton A, Gaham J. The modern law of trade marks. London: Butterworth 2005.
128. WIPO. WIPO Database of Intellectual Property and Legislative Texts.
129. WIPO.; World Intellectual Property Organization.
130. Rowland D, Campbell A. Supply of software: Copyright and Contract Issues. Int J Law Inform Technol 2002; 10: 23-40(18).
131. Davidson SJ, Bergs SJ, Kapsner M. Open, click, download, send. What have you agreed to? The possibilities seem endless. Computer Associations Law Conference (CLA 2001). 2001.
132. IPR-Helpdesk.; B Papers, ed., 2003; Vol. 10/20/2007, pp. 2-3.
133. OECD.; OECD, 2005; Vol. 2007.