QSAR and molecular docking techniques for the discovery of potent monoamine oxidase B inhibitors: Computer-aided generation of new rasagiline bioisosteres

Current Topics in Medicinal Chemistry

Volumn 12, Issue 16, 2012, Pages 1734-1747

  Speck Planche, Alejandro ^a   Kleandrova, Valeria V ^b  

^a UNIVERSITY OF PORTO   (Portugal)

^b MOSCOW STATE UNIVERSITY OF FOOD PRODUCTION   (Russian Federation)

Author keywords

Alzheimer; Artificial neural networks; Computer aided drug design; Molecular docking; Monoamine Oxidase B; Neurodegenerative disorders; Neuroprotective; Parkinson; QSAR; Rasagiline

Indexed keywords

ACETYLCHOLINESTERASE; BRAIN DERIVED NEUROTROPHIC FACTOR; CHOLINESTERASE; COUMARIN DERIVATIVE; GLIAL CELL LINE DERIVED NEUROTROPHIC FACTOR; LADOSTIGIL; MONOAMINE OXIDASE B INHIBITOR; N (1 INDENYL) N METHYL N PROPARGYLAMINE; NEUROPROTECTIVE AGENT; RASAGILINE; RIVASTIGMINE; SELEGILINE; SULFADIAZINE;

ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL ACTIVITY; DIFFUSE LEWY BODY DISEASE; DRUG DESIGN; HUMAN; MOLECULAR DOCKING; NUCLEAR MAGNETIC RESONANCE; PARKINSON DISEASE; PERCEPTRON; PHYSICAL CHEMISTRY; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RANDOMIZED CONTROLLED TRIAL (TOPIC); X RAY CRYSTALLOGRAPHY;

COMPUTER-AIDED DESIGN; DRUG DISCOVERY; INDANS; MOLECULAR DOCKING SIMULATION; MONOAMINE OXIDASE; MONOAMINE OXIDASE INHIBITORS; QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP;

EID: 84875331981     PISSN: 15680266     EISSN: 18734294     Source Type: Journal    
DOI: 10.2174/156802612803989282     Document Type: Article

References (171)

1. Byrne, S.C.; Rowland, L.P.; Vonsattel, J.P.G.; Welzel, A.T.; Walsh, D.M.; Hardiman, O. Common Themes in the Pathogenesis of Neurodegeneration. In Neurodegenerative Disorders: A Clinical Guide, Hardiman, O.; Doherty, C.P., Eds. Springer-Verlag London Limited: London, Dordrecht, Heidelberg, New York, 2011; pp 1-15.
2. Geula, C.; Mesulam, M.M. Cholinesterases and the pathology of Alzheimer disease. Alzheimer Dis. Assoc. Disord., 1995, 9, 23-28.
3. Brookmeyer, R.; Gray, S.; Kawas, C. Projections of Alzheimer's disease in the United States and the public health impact of delaying disease onset. Am. J. Public Health, 1998, 88, 1337-1342.
4. Klein, W.L. A Toxicity in Alzheimer's Disease. In Molecular Mechanisms of Neurodegenerative Diseases, Chesselet, M.F., Ed. Humana Press Inc: Totowa, New Jersey, 2011; pp 1-49.
5. nd ed.; Facts On File, Inc: New York, 2002.
6. de Lau, L.M.; Breteler, M.M. Epidemiology of Parkinson's disease. Lancet Neurol., 2006, 5, 525-535.
7. Apweiler, R.; Wu, C.; Xenarios, I. UniProtKB. http://www.uniprot. org/, 2002-2011.
8. Speck-Planche, A.; Luan, F.; Cordeiro, M.N. Role of Ligand-Based Drug Design Methodologies toward the Discovery of New Anti-Alzheimer Agents: Futures Perspectives in Fragment-Based Ligand Design. Curr. Med. Chem., 2012, 19, 1635-1645.
9. Mangoni, A.; Grassi, M.P.; Frattola, L.; Piolti, R.; Bassi, S.; Motta, A.; Marcone, A.; Smirne, S. Effects of a MAO-B inhibitor in the treatment of Alzheimer disease. Eur. Neurol., 1991, 31, 100-107.
10. Gulyas, B.; Pavlova, E.; Kasa, P.; Gulya, K.; Bakota, L.; Varszegi, S.; Keller, E.; Horvath, M.C.; Nag, S.; Hermecz, I.; Magyar, K.; Halldin, C. Activated MAO-B in the brain of Alzheimer patients, demonstrated by [11C]-L-deprenyl using whole hemisphere autoradiography. Neurochem. Int., 2011, 58, 60-68.
11. Muck-Seler, D.; Presecki, P.; Mimica, N.; Mustapic, M.; Pivac, N.; Babic, A.; Nedic, G.; Folnegovic-Smalc, V. Platelet serotonin concentration and monoamine oxidase type B activity in female patients in early, middle and late phase of Alzheimer's disease. Prog. Neuropsychopharmacol. Biol. Psychiatry, 2009, 33, 1226-1231.
12. Hirvonen, J.; Kailajarvi, M.; Haltia, T.; Koskimies, S.; Nagren, K.; Virsu, P.; Oikonen, V.; Sipila, H.; Ruokoniemi, P.; Virtanen, K.; Scheinin, M.; Rinne, J.O. Assessment of MAO-B occupancy in the brain with PET and [11C]-L-deprenyl-D2: a dose-finding study with a novel MAO-B inhibitor, EVT 301. Clin. Pharmacol. Ther., 2009, 85, 506-512.
13. Weinreb, O.; Mandel, S.; Bar-Am, O.; Yogev-Falach, M.; Avramovich-Tirosh, Y.; Amit, T.; Youdim, M.B. Multifunctional neuroprotective derivatives of rasagiline as anti-Alzheimer's disease drugs. Neurotherapeutics, 2009, 6, 163-174.
14. EBI-Team. ChEMBL Database. http://www.ebi.ac.uk/chembldb/, 2010.
15. Kubinyi, H. QSAR: Hansch analysis and related approaches, VCH Publishers: Weinheim, New York, Basel, Cambridge, Tokyo, 1993.
16. Riera-Fernandez, P.; Martin-Romalde, R.; Prado-Prado, F.J.; Escobar, M.; Munteanu, C.R.; Concu, R.; Duardo-Sanchez, A.; Gonzalez-Diaz, H. From QSAR models of drugs to complex networks: state-of-art review and introduction of new Markovspectral moments indices. Curr. Top. Med. Chem., 2012, 12, 927-960.
17. Chan, L.W.; Wang, F.F.; Cho, W.C. Genomic sequence analysis of EGFR regulation by microRNAs in lung cancer. Curr. Top. Med. Chem., 2012, 12, 920-926.
18. Garcia, I.; Fall, Y.; Gomez, G. Review of synthesis, biological assay, and QSAR studies of HMGR inhibitors. Curr. Top. Med. Chem., 2012, 12, 895-919.
19. Luan, F.; Borges, F.; Cordeiro, M.N. Recent advances on A(3) adenosine receptor antagonists by QSAR tools. Curr. Top. Med. Chem., 2012, 12, 878-894.
20. Moura Barbosa, A.J.; Del Rio, A. Freely accessible databases of commercial compounds for high-throughput virtual screenings. Curr. Top. Med. Chem., 2012, 12, 866-877.
21. Castillo-Garit, J.A.; Abad, C.; Rodriguez-Borges, J.E.; Marrero-Ponce, Y.; Torrens, F. A review of QSAR studies to discover new drug-like compounds actives against leishmaniasis and trypanosomiasis. Curr. Top. Med. Chem., 2012, 12, 852-865.
22. Dave, K.; Lahiry, A. Conotoxins: review and docking studies to determine potentials of conotoxin as an anticancer drug molecule. Curr. Top. Med. Chem., 2012, 12, 845-851.
23. Nino, H.; Rodriguez-Borges, J.E.; Garcia-Mera, X.; Prado-Prado, F. Review of synthesis, assay, and prediction of beta and gammasecretase inhibitors. Curr. Top. Med. Chem., 2012, 12, 828-844.
24. Khan, M.T.; Mischiati, C.; Ather, A.; Ohyama, T.; Dedachi, K.; Borgatti, M.; Kurita, N.; Gambari, R. Structure-based analysis of the molecular recognitions between HIV-1 TAR-RNA and transcription factor nuclear factor-kappaB (NFkB). Curr. Top. Med. Chem., 2012, 12, 814-827.
25. Gonzalez-Diaz, H. Editorial: QSAR/QSPR models as enabling technologies for drug & targets discovery in: medicinal chemistry, microbiology-parasitology, neurosciences, bioinformatics, proteo mics and other biomedical sciences. Curr. Top. Med. Chem., 2012, 12, 799-801.
26. Costa, B.; Da Pozzo, E.; Martini, C. Translocator protein as a promising target for novel anxiolytics. Curr. Top. Med. Chem., 2012, 12, 270-285.
27. Speck-Planche, A.; Cordeiro, M.N. Computer-aided drug design methodologies toward the design of anti-hepatitis C agents. Curr. Top. Med. Chem., 2012, 12, 802-813.
28. 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.
29. 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.
30. 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.
31. 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.
32. 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.
33. 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, applica tions, and legal issues. Curr. Pharm. Des., 2010, 16, 2737-2764.
34. Gonzalez-Diaz, H. QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neuro sciences. Curr. Pharm. Des., 2010, 16, 2598-2600.
35. Garcia, I.; Fall, Y.; Gomez, G. QSAR, docking, and CoMFA studies of GSK3 inhibitors. Curr. Pharm. Des., 2010, 16, 2666-2675.
36. Galdeano, C.; Viayna, E.; Arroyo, P.; Bidon-Chanal, A.; Blas, J.R.; Munoz-Torrero, D.; Luque, F.J. Structural determinants of the multifunctional profile of dual binding site acetylcholinesterase inhibitors as anti-Alzheimer agents. Curr. Pharm. Des., 2010, 16, 2818-2836.
37. 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.
38. 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.
39. 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.
40. Wang, J.F.; Chou, K.C. Molecular modeling of cytochrome P450 and drug metabolism. Curr. Drug Metab., 2010, 11, 342-346.
41. 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.
42. 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.
43. 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.
44. 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.
45. Gonzalez-Diaz, H. Network topological indices, drug metabolism, and distribution. Curr. Drug Metab., 2010, 11, 283-284.
46. Garcia, I.; Diop, Y.F.; Gomez, G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr. Drug Metab., 2010, 11, 307-314.
47. Chou, K.C. Graphic rule for drug metabolism systems. Curr. Drug Metab., 2010, 11, 369-378.
48. Jahnke, W.; Erlanson, D.A. Fragment-based Approaches in Drug Discovery, Wiley-VCH Verlag GmbH & Co. KGaA: Weinheim, Germany, 2006.
49. Oprea, T. Chemoinformatics in Drug Discovery, WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2005.
50. Todeschini, R.; Consonni, V. Molecular Descriptors for Chemoin formatics, WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2009.
51. Larson, R.S. Bioinformatics and Drug Discovery, Humana Press Inc: Totowa, New Jersey, 2006.
52. Polanski, A.; Kimmel, M. Bioinformatics, Springer-Verlag: Berlin, Heidelberg, New York, 2007.
53. Gonzalez-Diaz, H.; Munteanu, C.R. Topological Indices for Medicinal Chemistry, Biology, Parasitology, Neurological and Social Networks, Transworld Research Network: Kerala, 2010.
54. Hansch, C.; Leo, A. Exploring QSAR: fundamentals and appli cations in Chemistry and Biology, American Chemical Society: Washington, DC, 1995.
55. van de Waterbeemd, H. Chemometrics methods in molecular design, VCH Publishers: Weinheim, New York, Basel, Cambridge, Tokyo, 1995.
56. Gasteiger, J. Handbook of Chemoinformatics, WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2003.
57. Fernandez, M.; Tundidor-Camba, A.; Caballero, J. Modeling of cyclin-dependent kinase inhibition by 1H-pyrazolo[3,4-d]pyrimi dine derivatives using artificial neural network ensembles. J. Chem. Inf. Model., 2005, 45, 1884-1895.
58. Gonzalez, M.P.; Caballero, J.; Helguera, A.M.; Garriga, M.; Gonzalez, G.; Fernandez, M. 2D autocorrelation modelling of the inhibitory activity of cytokinin-derived cyclin-dependent kinase inhibitors. Bull. Math. Biol., 2006, 68, 735-751.
59. Caballero, J.; Fernandez, M. Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks. J. Mol. Model., 2006, 12, 168-181.
60. Goulon, A.; Picot, T.; Duprat, A.; Dreyfus, G. Predicting activities without computing descriptors: graph machines for QSAR. SAR QSAR Environ. Res., 2007, 18, 141-153.
61. Du, H.; Wang, J.; Hu, Z.; Yao, X.; Zhang, X. Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression. J. Agric. Food Chem., 2008, 56, 10785-10792.
62. Munteanu, C.R.; Gonzalez-Diaz, H.; Magalhaes, A.L. Enzymes/non-enzymes classification model complexity based on composi tion, sequence, 3D and topological indices. J. Theor. Biol., 2008, 254, 476-482.
63. Tetko, I.V.; Sushko, I.; Pandey, A.K.; Zhu, H.; Tropsha, A.; Papa, E.; Oberg, T.; Todeschini, R.; Fourches, D.; Varnek, A. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J. Chem. Inf. Model., 2008, 48, 1733-1746.
64. Roy, K.; Mandal, A.S. Predictive QSAR modeling of CCR5 antagonist piperidine derivatives using chemometric tools. J. Enzyme Inhib. Med. Chem., 2009, 24, 205-223.
65. Scotti, M.T.; Fernandes, M.B.; Ferreira, M.J.; Emerenciano, V.P. Quantitative structure-activity relationship of sesquiterpene lactones with cytotoxic activity. Bioorg. Med. Chem., 2007, 15, 2927-2934.
66. Cao, H.; Cao, R.; Zhang, H.; Zheng, X.; Gao, D. Non-nucleoside inhibitors of NS5B polymerase binding to allosteric sites: 3DQSAR and molecular docking studies. Curr. Med. Chem., 2008, 15, 1462-1477.
67. Casañola-Martin, G.M.; Marrero-Ponce, Y.; Khan, M.T.; Khan, S.B.; Torrens, F.; Perez-Jimenez, F.; Rescigno, A.; Abad, C. Bondbased 2D quadratic fingerprints in QSAR studies: virtual and in vitro tyrosinase inhibitory activity elucidation. Chem. Biol. Drug Des., 2010, 76, 538-545.
68. Marrero-Ponce, Y.; Meneses-Marcel, A.; Rivera-Borroto, O.M.; Garcia-Domenech, R.; De Julian-Ortiz, J.V.; Montero, A.; Escario, J.A.; Barrio, A.G.; Pereira, D.M.; Nogal, J.J.; Grau, R.; Torrens, F.; Vogel, C.; Aran, V.J. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds. J. Comput. Aided Mol. Des., 2008, 22, 523-540.
69. Santana, L.; Uriarte, E.; Gonzalez-Diaz, H.; Zagotto, G.; Soto-Otero, R.; Mendez-Alvarez, E. A QSAR model for in silico screening of MAO-A inhibitors. Prediction, synthesis, and biolo gical assay of novel coumarins. J. Med. Chem, 2006, 49, 1149-1156.
70. Speck-Planche, A.; Cordeiro, M.N. Current Drug Design of Anti-HIV Agents Through the Inhibition of C-C Chemokine Receptor Type 5. Curr. Comput. Aided Drug Des., 2011, 7, 238-248.
71. Speck-Planche, A.; Cordeiro, M.N.; Guilarte-Montero, L.; Yera-Bueno, R. Current computational approaches towards the rational design of new insecticidal agents. Curr. Comput. Aided Drug Des., 2011, 7, 304-314.
72. 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.
73. Speck-Planche, A.; Guilarte-Montero, L.; Yera-Bueno, R.; Rojas-Vargas, J.A.; García-López, A.; Uriarte, E.; Molina-Pérez, E. Rational design of new agrochemical fungicides using substructural descriptors. Pest Manag. Sci., 2011, 67, 438-445.
74. Speck-Planche, A.; Luan, F.; Cordeiro, M.N. Discovery of Anti-Alzheimer Agents: Current Ligand-Based Approaches toward the Design of Acetylcholinesterase Inhibitors. Mini Rev Med Chem, 2012, 12, 583-591.
75. Gonzalez-Diaz, H.; Aguero-Chapin, G.; Varona, J.; Molina, R.; Delogu, G.; Santana, L.; Uriarte, E.; Podda, G. 2D-RNA-coupling numbers: a new computational chemistry approach to link secondary structure topology with biological function. J. Comput. Chem., 2007, 28, 1049-1056.
76. Gonzalez-Diaz, H.; Aguero-Chapin, G.; Varona-Santos, J.; Molina, R.; de la Riva, G.; Uriarte, E. 2D RNA-QSAR: assigning ACC oxidase family membership with stochastic molecular descriptors; isolation and prediction of a sequence from Psidium guajava L. Bioorg. Med. Chem. Lett., 2005, 15, 2932-2937.
77. 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.
78. Gonzalez-Diaz, H.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Prado-Prado, F.J.; Aguero-Chapin, G.; Bolas-Fernandez, F.; Vazquez-Padron, R.I.; Ubeira, F.M. QSAR for RNases and theoretic-experimental study of molecular diversity on peptide mass fingerprints of a new Leishmania infantum protein. Mol. Divers., 2009, 14, 349-369.
79. Gonzalez-Diaz, H.; Molina, R.; Uriarte, E. Markov entropy backbone electrostatic descriptors for predicting proteins biological activity. Bioorg. Med. Chem. Lett., 2004, 14, 4691-4695.
80. González-Díaz, H.; Pérez-Bello, A.; Cruz-Monteagudo, M.; González-Díaz, Y.; Santana, L.; Uriarte, E. Chemometrics for QSAR with low sequence homology: Mycobacterial promoter sequences recognition with 2D-RNA entropies. Chemometr. Intell. Lab. Syst., 2007, 85, 20-26.
81. Estrada, E.; Pena, A.; Garcia-Domenech, R. Designing sedative/hypnotic compounds from a novel substructural graphtheoretical approach. J. Comput. Aided Mol. Des., 1998, 12, 583-595.
82. Estrada, E.; Peña, A. In silico studies for the rational discovery of anticonvulsant compounds. Bioorg. Med. Chem., 2000, 8, 2755-2770.
83. 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.
84. Estrada, E.; Uriarte, E.; Montero, A.; Teijeira, M.; Santana, L.; De Clercq, E. A novel approach for the virtual screening and rational design of anticancer compounds. J. Med. Chem, 2000, 43, 1975-1985.
85. Garcia, I.; Fall, Y.; Gomez, G.; Gonzalez-Diaz, H. First computational chemistry multi-target model for anti-Alzheimer, anti-parasitic, anti-fungi, and anti-bacterial activity of GSK-3 inhibitors in vitro, in vivo, and in different cellular lines. Mol. Divers., 2011, 15, 561-567.
86. González-Díaz, H.; Cabrera-Pérez, M.A.; Agüero-Chapín, G.; Cruz-Monteagudo, M.; Castañeda-Cancio, N.; del Río, M.A.; Uriarte, E. Multi-target QSPR assemble of a Complex Network for the distribution of chemicals to biphasic systems and biological tissues. Chemometr. Intell. Lab. Syst., 2008, 94, 160-165.
87. Gonzalez-Diaz, H.; Prado-Prado, F.J. Unified QSAR and networkbased computational chemistry approach to antimicrobials, part 1: multispecies activity models for antifungals. J. Comput. Chem., 2008, 29, 656-667.
88. Gonzalez-Diaz, H.; Prado-Prado, F.J.; Santana, L.; Uriarte, E. Unify QSAR approach to antimicrobials. Part 1: predicting antifungal activity against different species. Bioorg. Med. Chem., 2006, 14, 5973-5980.
89. Munteanu, C.R.; Magalhaes, A.L.; Uriarte, E.; Gonzalez-Diaz, H. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. J. Theor. Biol., 2009, 257, 303-311.
90. Prado-Prado, F.; Garcia-Mera, X.; Abeijon, P.; Alonso, N.; Caamano, O.; Yanez, M.; Garate, T.; Mezo, M.; Gonzalez-Warleta, M.; Muino, L.; Ubeira, F.M.; Gonzalez-Diaz, H. Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica. Eur. J. Med. Chem., 2011, 46, 1074-1094.
91. Prado-Prado, F.; Garcia-Mera, X.; Escobar, M.; Sobarzo-Sanchez, E.; Yanez, M.; Riera-Fernandez, P.; Gonzalez-Diaz, H. 2D MIDRAGON: a new predictor for protein-ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. Eur. J. Med. Chem., 2011, 46, 5838-5851.
92. Prado-Prado, F.J.; Borges, F.; Perez-Montoto, L.G.; Gonzalez-Diaz, H. Multi-target spectral moment: QSAR for antifungal drugs vs. different fungi species. Eur. J. Med. Chem., 2009, 44, 4051-4056.
93. Prado-Prado, F.J.; Borges, F.; Uriarte, E.; Perez-Montoto, L.G.; Gonzalez-Diaz, H. Multi-target spectral moment: QSAR for antiviral drugs vs. different viral species. Anal. Chim. Acta, 2009, 651, 159-164.
94. Prado-Prado, F.J.; García, I.; García-Mera, G.; González-Díaz, H. Entropy multi-target QSAR model for prediction of antiviral drug complex networks. Chemometr. Intell. Lab. Syst., 2011, 107, 227-233.
95. Prado-Prado, F.J.; 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, 2225-2231.
96. 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.
97. Prado-Prado, F.J.; Martinez de la Vega, O.; 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.
98. 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, 825-835.
99. Speck-Planche, A.; Kleandrova, V.V. In silico design of multitarget inhibitors for C-C chemokine receptors using substructural descriptors. Mol. Divers., 2012, 16, 183-191.
100. Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Multi-target drug discovery in anti-cancer therapy: Fragment-based approach toward the design of potent and versatile anti-prostate cancer agents. Bioorg. Med. Chem., 2011, 19, 6239-6244.
101. Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Fragment-based QSAR model toward the selection of versatile anti-sarcoma leads. Eur. J. Med. Chem, 2011, 46, 5910-5916.
102. Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Chemoinformatics in Multi-Target Drug Discovery for Anti-Cancer Therapy: In Silico Design Of Potent And Versatile Anti-Brain Tumor Agents. Anticancer Agents Med. Chem., 2011, 12, 678-685.
103. Speck-Planche, A.; Kleandrova, V.V.; Luan, F.; Cordeiro, M.N. Predicting multiple ecotoxicological profiles in agrochemical fungicides: A multi-species chemoinformatic approach. Ecotoxicol. Environ. Saf., 2012, 80, 308-313.
104. Speck-Planche, A.; Kleandrova, V.V.; Rojas-Vargas, J.A. QSAR model toward the rational design of new agrochemical fungicides with a defined resistance risk using substructural descriptors. Mol. Divers., 2011, 15, 901-909.
105. Speck-Planche, A.; Kleandrova, V.V.; Scotti, M.T. Fragment-based approach for the in silico discovery of multi-target insecticides. Chemometr. Intell. Lab. Syst., 2012, 111, 39-45.
106. Cruz-Monteagudo, M.; Gonzalez-Diaz, H.; Aguero-Chapin, G.; Santana, L.; Borges, F.; Dominguez, E.R.; Podda, G.; Uriarte, E. Computational chemistry development of a unified free energy Markov model for the distribution of 1300 chemicals to 38 different environmental or biological systems. J. Comput. Chem., 2007, 28, 1909-1923.
107. Concu, R.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Bolas-Fernandez, F.; Prado-Prado, F.J.; Podda, G.; Uriarte, E.; Ubeira, F.M.; Gonzalez-Diaz, H. Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins. J. Proteome Res., 2009, 8, 4372-4382.
108. Concu, R.; Dea-Ayuela, M.A.; Perez-Montoto, L.G.; Prado-Prado, F.J.; Uriarte, E.; Bolas-Fernandez, F.; Podda, G.; Pazos, A.; Munteanu, C.R.; Ubeira, F.M.; Gonzalez-Diaz, H. 3D entropy and moments prediction of enzyme classes and experimental-theoretic study of peptide fingerprints in Leishmania parasites. Biochim. Biophys. Acta, 2009, 1794, 1784-1794.
109. Concu, R.; Podda, G.; Uriarte, E.; Gonzalez-Diaz, H. Compu tational 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, 30, 1510-1520.
110. Dehmer, M.; Emmert-Streib, F. Analysis of Complex Networks. From Biology to Linguistics, WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2009.
111. 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.
112. Barabasi, A.L. Scale-free networks: a decade and beyond. Science, 2009, 325, 412-413.
113. Estrada, E. Generalized walks-based centrality measures for complex biological networks. J. Theor. Biol., 2010, 263, 556-565.
114. Estrada, E. Universality in protein residue networks. Biophys. J., 2010, 98, 890-900.
115. Garcia, I.; Munteanu, C.R.; Fall, Y.; Gomez, G.; Uriarte, E.; Gonzalez-Diaz, H. QSAR and complex network study of the chiral HMGR inhibitor structural diversity. Bioorg. Med. Chem., 2009, 17, 165-175.
116. Prado-Prado, F.J.; Ubeira, F.M.; 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, 164-173.
117. Prado-Prado, F.J.; Uriarte, E.; Borges, F.; Gonzalez-Diaz, H. Multitarget spectral moments for QSAR and Complex Networks study of antibacterial drugs. Eur. J. Med. Chem, 2009, 44, 4516-4521.
118. Santana, L.; Gonzalez-Diaz, H.; Quezada, E.; Uriarte, E.; Yanez, M.; Vina, D.; Orallo, F. Quantitative structure-activity relationship and complex network approach to monoamine oxidase A and B inhibitors. J. Med. Chem, 2008, 51, 6740-6751.
119. Gonzalez-Diaz, H.; Muino, L.; Anadon, A.M.; Romaris, F.; Prado-Prado, F.J.; Munteanu, C.R.; Dorado, J.; Sierra, A.P.; Mezo, M.; Gonzalez-Warleta, M.; Garate, T.; Ubeira, F.M. MISS-Prot: web server for self/non-self discrimination of protein residue networks in parasites; theory and experiments in Fasciola peptides and Anisakis allergens. Mol. Biosyst., 2011, 7, 1938-1955.
120. Gonzalez-Diaz, H.; Munteanu, C.R.; Postelnicu, L.; Prado-Prado, F.; Gestal, M.; Pazos, A. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria. Mol. Biosyst., 2012, 8, 851-862.
121. Gonzalez-Diaz, H.; Prado-Prado, F.; Garcia-Mera, X.; Alonso, N.; Abeijon, P.; Caamano, O.; Yanez, M.; Munteanu, C.R.; Pazos, A.; Dea-Ayuela, M.A.; Gomez-Munoz, M.T.; Garijo, M.M.; Sansano, J.; Ubeira, F.M. MIND-BEST: Web Server for Drugs and Target Discovery; Design, Synthesis, and Assay of MAO-B Inhibitors and Theoretical-Experimental Study of G3PDH Protein from Tricho monas gallinae. J. Proteome Res., 2011, 10, 1698-1718.
122. Gonzalez-Diaz, H.; Prado-Prado, F.; Sobarzo-Sanchez, E.; Haddad, M.; Maurel Chevalley, S.; Valentin, A.; Quetin-Leclercq, J.; Dea-Ayuela, M.A.; Teresa Gomez-Munos, M.; Munteanu, C.R.; Jose Torres-Labandeira, J.; Garcia-Mera, X.; Tapia, R.A.; Ubeira, F.M. NL MIND-BEST: A web server for ligands and proteins discovery-Theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. J. Theor. Biol., 2011, 276, 229-249.
123. Rodriguez-Soca, Y.; Munteanu, C.R.; Dorado, J.; Pazos, A.; Prado-Prado, F.J.; 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, 1182-1190.
124. Kubinyi, H.; Folkers, G.; Martin, Y.C. 3D QSAR in Drug Design. Recent Advances, Kluwer Academic Publishers: New York, Boston, Dordrecht, London, Moscow, 2002.
125. Cruciani, G. Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction. WILEY-VCH Verlag GmbH & Co. KGaA: Weinheim, 2006.
126. Kneubuhler, S.; Thull, U.; Altomare, C.; Carta, V.; Gaillard, P.; Carrupt, P.A.; Carotti, A.; Testa, B. Inhibition of monoamine oxidase-B by 5H-indeno[1,2-c]pyridazines: biological activities, quantitative structure-activity relationships (QSARs) and 3DQSARs. J. Med. Chem, 1995, 38, 3874-3883.
127. Medvedev, A.E.; Ivanov, A.S.; Veselovsky, A.V.; Skvortsov, V.S.; Archakov, A.I. QSAR analysis of indole analogues as monoamine oxidase inhibitors. J. Chem. Inf. Comput. Sci., 1996, 36, 664-671.
128. Altomare, C.; Cellamare, S.; Summo, L.; Catto, M.; Carotti, A.; Thull, U.; Carrupt, P.A.; Testa, B.; Stoeckli-Evans, H. Inhibition of monoamine oxidase-B by condensed pyridazines and pyrimidines: effects of lipophilicity and structure-activity relationships. J. Med. Chem, 1998, 41, 3812-3820.
129. Medvedev, A.E.; Veselovsky, A.V.; Shvedov, V.I.; Tikhonova, O.V.; Moskvitina, T.A.; Fedotova, O.A.; Axenova, L.N.; Kamyshanskaya, N.S.; Kirkel, A.Z.; Ivanov, A.S. Inhibition of monoamine oxidase by pirlindole analogues: 3D-QSAR and CoMFA analysis. J. Chem. Inf. Comput. Sci., 1998, 38, 1137-1144.
130. Gnerre, C.; Catto, M.; Leonetti, F.; Weber, P.; Carrupt, P.A.; Altomare, C.; Carotti, A.; Testa, B. Inhibition of monoamine oxidases by functionalized coumarin derivatives: biological activities, QSARs, and 3D-QSARs. J. Med. Chem., 2000, 43, 4747-4758.
131. Moron, J.A.; Campillo, M.; Perez, V.; Unzeta, M.; Pardo, L. Molecular determinants of MAO selectivity in a series of indolylmethylamine derivatives: biological activities, 3DQSAR/CoMFA analysis, and computational simulation of ligand recognition. J. Med. Chem., 2000, 43, 1684-1691.
132. Gritsch, S.; Guccione, S.; Hoffmann, R.; Cambria, A.; Raciti, G.; Langer, T. A 3D QSAR study of monoamino oxidase-B inhibitors using the chemical function based pharmacophore generation approach. J. Enzyme Inhib., 2001, 16, 199-215.
133. Carrieri, A.; Carotti, A.; Barreca, M.L.; Altomare, C. Binding models of reversible inhibitors to type-B monoamine oxidase. J. Comput. Aided Mol. Des., 2002, 16, 769-778.
134. Chimenti, F.; Secci, D.; Bolasco, A.; Chimenti, P.; Granese, A.; Carradori, S.; Maccioni, E.; Cardia, M.C.; Yanez, M.; Orallo, F.; Alcaro, S.; Ortuso, F.; Cirilli, R.; Ferretti, R.; Distinto, S.; Kirchmair, J.; Langer, T. Synthesis, semipreparative HPLC separation, biological evaluation, and 3D-QSAR of hydrazothiazole derivatives as human monoamine oxidase B inhibitors. Bioorg. Med. Chem., 2010, 18, 5063-5070.
135. Strydom, B.; Malan, S.F.; Castagnoli, N., Jr.; Bergh, J.J.; Petzer, J.P. Inhibition of monoamine oxidase by 8-benzyloxycaffeine analogues. Bioorg. Med. Chem., 2010, 18, 1018-1028.
136. Gonzalez-Diaz, H.; Prado-Prado, F.; Garcia-Mera, X.; Alonso, N.; Abeijon, P.; Caamano, O.; Yanez, M.; Munteanu, C.R.; Pazos, A.; Dea-Ayuela, M.A.; Gomez-Munoz, M.T.; Garijo, M.M.; Sansano, J.; Ubeira, F.M. MIND-BEST: Web server for drugs and target discovery; design, synthesis, and assay of MAO-B inhibitors and theoretical-experimental study of G3PDH protein from Tricho monas gallinae. J. Proteome Res., 2011, 10, 1698-1718.
137. Gonzalez-Diaz, H.; Bonet, I.; Teran, C.; De Clercq, E.; Bello, R.; Garcia, M.M.; Santana, L.; Uriarte, E. ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds. Eur. J. Med. Chem, 2007, 42, 580-585.
138. Vilar, S.; Santana, L.; Uriarte, E. Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action. J. Med. Chem, 2006, 49, 1118-1124.
139. Patra, J.C.; Singh, O. Artificial neural networks-based approach to design ARIs using QSAR for diabetes mellitus. J. Comput. Chem., 2009, 30, 2494-2508.
140. Doucet, J.P.; Weber, J. Computer-Aided Molecular Design: Theory and Applications, Academic Press: London, San Diego, New York, Boston, Sidney, Tokyo, Toronto, 1996.
141. Tripathi, A.; Surface, J.A.; Kellogg, G.E. Using Active Site Mapping and Receptor-Based Pharmacophore Tools: Prelude to Docking and De Novo/Fragment-Based Ligand Design. In Drug Design and Discovery: Methods and Protocols, Satyanarayanajois, S.D., Ed. Humana Press: New York, Dordrecht, Heidelberg, London, 2011; pp 39-54.
142. Chothia, C.; Lesk, A.M. The relation between the divergence of sequence and structure in proteins. EMBO J., 1986, 5, 823-826.
143. Moustakas, D.T.; Pegg, S.C.H.; Kuntz, I.D. A Practical Guide to DOCK 5. In Virtual Screening in Drug Discovery, Alvarez, J.; Shoichet, B., Eds. Taylor & Francis Group, LLC: Boca Raton, London, New York, Singapore, 2005; pp 303-326.
144. Vina, D.; Matos, M.J.; Ferino, G.; Cadoni, E.; Laguna, R.; Borges, F.; Uriarte, E.; Santana, L. 8-Substituted 3-arylcoumarins as potent and selective MAO-B inhibitors: synthesis, pharmacological evaluation, and docking studies. ChemMedChem, 2012, 7, 464-470.
145. Serra, S.; Ferino, G.; Matos, M.J.; Vazquez-Rodriguez, S.; Delogu, G.; Vina, D.; Cadoni, E.; Santana, L.; Uriarte, E. Hydroxycou marins as selective MAO-B inhibitors. Bioorg. Med. Chem. Letters, 2012, 22, 258-261.
146. Matos, M.J.; Teran, C.; Perez-Castillo, Y.; Uriarte, E.; Santana, L.; Vina, D. Synthesis and study of a series of 3-arylcoumarins as potent and selective monoamine oxidase B inhibitors. J. Med. Chem., 2011, 54, 7127-7137.
147. Maccioni, E.; Alcaro, S.; Cirilli, R.; Vigo, S.; Cardia, M.C.; Sanna, M.L.; Meleddu, R.; Yanez, M.; Costa, G.; Casu, L.; Matyus, P.; Distinto, S. 3-Acetyl-2,5-diaryl-2,3-dihydro-1,3,4-oxadiazoles: a new scaffold for the selective inhibition of monoamine oxidase B. J. Med. Chem, 2011, 54, 6394-6398.
148. Gaspar, A.; Silva, T.; Yanez, M.; Vina, D.; Orallo, F.; Ortuso, F.; Uriarte, E.; Alcaro, S.; Borges, F. Chromone, a privileged scaffold for the development of monoamine oxidase inhibitors. J. Med. Chem., 2011, 54, 5165-5173.
149. Delogu, G.; Picciau, C.; Ferino, G.; Quezada, E.; Podda, G.; Uriarte, E.; Vina, D. Synthesis, human monoamine oxidase inhibi tory activity and molecular docking studies of 3-heteroaryl coumarin derivatives. Eur. J. Med. Chem., 2011, 46, 1147-1152.
150. Lühr, S.; Vilches-Herrera, M.; Fierro, A.; Ramsay, R.R.; Edmond son, D.E.; Reyes-Parada, M.; Cassels, B.K.; Iturriaga-Vasquez, P. 2-Arylthiomorpholine derivatives as potent and selective monoa mine oxidase B inhibitors. Bioorg. Med. Chem., 2010, 18, 1388-1395.
151. Geldenhuys, W.J.; Darvesh, A.S.; Funk, M.O.; Van der Schyf, C.J.; Carroll, R.T. Identification of novel monoamine oxidase B inhibitors by structure-based virtual screening. Bioorg. Med. Chem. Letters, 2010, 20, 5295-5298.
152. Chimenti, F.; Fioravanti, R.; Bolasco, A.; Chimenti, P.; Secci, D.; Rossi, F.; Yanez, M.; Orallo, F.; Ortuso, F.; Alcaro, S.; Cirilli, R.; Ferretti, R.; Sanna, M.L. A new series of flavones, thioflavones, and flavanones as selective monoamine oxidase-B inhibitors. Bioorg. Med. Chem., 2010, 18, 1273-1279.
153. Alcaro, S.; Gaspar, A.; Ortuso, F.; Milhazes, N.; Orallo, F.; Uriarte, E.; Yanez, M.; Borges, F. Chromone-2-and-3-carboxylic acids inhibit differently monoamine oxidases A and B. Bioorg. Med. Chem. Letters, 2010, 20, 2709-2712.
154. Boppana, K.; Dubey, P.K.; Jagarlapudi, S.A.; Vadivelan, S.; Rambabu, G. Knowledge based identification of MAO-B selective inhibitors using pharmacophore and structure based virtual screening models. Eur. J. Med. Chem., 2009, 44, 3584-3590.
155. Leonetti, F.; Capaldi, C.; Pisani, L.; Nicolotti, O.; Muncipinto, G.; Stefanachi, A.; Cellamare, S.; Caccia, C.; Carotti, A. Solid-phase synthesis and insights into structure-activity relationships of safinamide analogues as potent and selective inhibitors of type B monoamine oxidase. J. Med. Chem., 2007, 50, 4909-4916.
156. Catto, M.; Nicolotti, O.; Leonetti, F.; Carotti, A.; Favia, A.D.; Soto-Otero, R.; Mendez-Alvarez, E. Structural insights into monoamine oxidase inhibitory potency and selectivity of 7-substituted coumarins from ligand-and target-based approaches. J. Med. Chem., 2006, 49, 4912-4925.
157. Finberg, J.P.; Lamensdorf, I.; Commissiong, J.W.; Youdim, M.B. Pharmacology and neuroprotective properties of rasagiline. J. Neural. Transm. Suppl., 1996, 48, 95-101.
158. Binda, C.; Hubalek, F.; Li, M.; Herzig, Y.; Sterling, J.; Edmond son, D.E.; Mattevi, A. Binding of rasagiline-related inhibitors to human monoamine oxidases: a kinetic and crystallographic analysis. J. Med. Chem., 2005, 48, 8148-8154.
159. Naoi, M.; Maruyama, W.; Youdim, M.B.; Yu, P.; Boulton, A.A. Anti-apoptotic function of propargylamine inhibitors of type-B monoamine oxidase. Inflammopharmacology, 2003, 11, 175-181.
160. Weinstock, M.; Bejar, C.; Wang, R.H.; Poltyrev, T.; Gross, A.; Finberg, J.P.; Youdim, M.B. TV3326, a novel neuroprotective drug with cholinesterase and monoamine oxidase inhibitory activities for the treatment of Alzheimer's disease. J. Neural. Transm. Suppl., 2000, 157-169.
161. Weinreb, O.; Amit, T.; Bar-Am, O.; Youdim, M.B. Induction of neurotrophic factors GDNF and BDNF associated with the mechanism of neurorescue action of rasagiline and ladostigil: new insights and implications for therapy. Ann. N. Y. Acad. Sci., 2007, 1122, 155-168.
162. Todeschini, R.; Consonni, V. Handbook of Molecular Descriptors, WILEY-VCH Verlag GmbH: Weinheim, New York, Chichester, Brisbane, Singapore, Toronto, 2000.
163. O'Neill, M.J.; Heckelman, P.E.; Koch, C.B.; Roman, K.J. The Merck Index, An Encyclopedia of Chemicals, Drugs and Biologicals, 14th ed.; Whitehouse Station, NJ: Merck & Co., Inc: New Jersey, 2006.
164. Todeschini, R.; Consonni, V.; Mauri, A.; Pavan, M. DRAGON for Windows (Software for Molecular Descriptor Calculations), v5.3; Milano Chemometrics and QSAR Research Group: Milano, 2005.
165. Munteanu, C.R.; Gonzalez-Diaz, H.; Borges, F.; de Magalhaes, A.L. Natural/random protein classification models based on star network topological indices. J. Theor. Biol., 2008, 254, 775-783.
166. StatSoft. STATISTICA. Data analysis software system, v6.0; Tulsa, 2001.
167. Hanczar, B.; Hua, J.; Sima, C.; Weinstein, J.; Bittner, M.; Dougherty, E.R. Small-sample precision of ROC-related estimates. Bioinformatics, 2010, 26, 822-830.
168. Aguero-Chapin, G.; Varona-Santos, J.; de la Riva, G.A.; Antunes, A.; Gonzalez-Vlla, T.; Uriarte, E.; Gonzalez-Diaz, H. Alignmentfree prediction of polygalacturonases with pseudofolding topological indices: experimental isolation from Coffea arabica and prediction of a new sequence. J. Proteome Res., 2009, 8, 2122-2128.
169. Garcia, I.; Fall, Y.; Garcia-Mera, X.; Prado-Prado, F. Theoretical study of GSK-3alpha: neural networks QSAR studies for the design of new inhibitors using 2D descriptors. Mol. Divers., 2011, 15, 947-955.
170. Garcia-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.
171. Patrick, G.L. An introduction to medicinal chemistry, Oxford University Press Inc.: Oxford, New York, Tokyo, 1995.