Review of Bioinformatics and QSAR studies of β-secretase inhibitors

Current Bioinformatics

Volumn 6, Issue 1, 2011, Pages 3-15

  Prado Prado, Francisco ^a   Escobar Cubiella, Manuel ^a   García Mera, Xerardo ^a  

^a UNIVERSITY OF SANTIAGO DE COMPOSTELA   (Spain)

Author keywords

Alzheimer's disease (AD); COMFA; CoMSIA; Docking; QSAR; secretase inhibitors; Topological indices

Indexed keywords

MOLECULAR GRAPHICS; NEURAL NETWORKS; NEURODEGENERATIVE DISEASES;

ALZHEIMER DISEASE; ALZHEIMERS DISEASE; COMFA; COMSIA; DOCKING; QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP; SECRETASES; SS-SECRETASE INHIBITOR; TOPOLOGICAL INDEX; Β-SECRETASE INHIBITORS;

COMPUTATIONAL CHEMISTRY;

BETA SECRETASE INHIBITOR; PEPTIDOMIMETIC AGENT;

ARTIFICIAL NEURAL NETWORK; BIOINFORMATICS; CHEMBL COMPUTER PROGRAM; COMPARATIVE MOLECULAR FIELD ANALYSIS; COMPARATIVE MOLECULAR SIMILARITY INDICES ANALYSIS; COMPUTER PROGRAM; MOLECULAR DOCKING; ONLINE SYSTEM; PDB COMPUTER PROGRAM; PRIORITY JOURNAL; PROTEIN DATABASE; PROTON TRANSPORT; QUANTITATIVE STRUCTURE ACTIVITY RELATION; RECEPTOR BINDING; REVIEW; SCREENING;

EID: 79953852944     PISSN: 15748936     EISSN: None     Source Type: Journal    
DOI: 10.2174/157489311795222428     Document Type: Review

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31. Ivanciuc O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr Top Med Chem 2008; 8: 1691-709.
32. 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.
33. Wang JF, Wei DQ, Chou KC. Drug candidates from traditional chinese medicines. Curr Top Med Chem 2008; 8: 1656-65.
34. Wang JF, Wei DQ, Chou KC. Pharmacogenomics and personalized use of drugs. Curr Top Med Chem 2008; 8: 1573-9.
35. Torrens F, Castellano G. Topological charge-transfer indices: from small molecules to proteins. Curr Proteomics 2009: 204-13.
36. Concu R, Dea-Ayuela MA, Perez-Montoto LG, et al. 3D entropy and moments prediction of enzyme classes and experimentaltheoretic study of peptide fingerprints in Leishmania parasites. Biochimica et Biophysica Acta 2009; 1794: 1784-94.
37. Ivanciuc O. Machine learning Quantitative Structure-Activity Relationships (QSAR) for peptides binding to human amphiphysin-1 SH3 domain. Curr Proteomics 2009; 4: 289-302.
38. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 2009; 261: 449-58.
39. Giuliani A, Di Paola L, Setola R. Proteins as networks: a mesoscopic approach using haemoglobin molecule as case study. Curr Proteomics 2009; 6: 235-45.
40. Chou KC. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteomics 2009; 6: 262-74.
41. Chen J, Shen B. Computational analysis of amino acid mutation: a proteome wide perspective. Curr Proteomics 2009; 6: 228-34.
42. Zhong WZ, 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.
43. Wang JF, Chou KC. Molecular modeling of cytochrome P450 and drug metabolism. Curr Drug Metab 2010; 11: 342-6.
44. 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: 315-41.
45. Martinez-Romero M, Vazquez-Naya JM, Rabunal JR, et al. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr Drug Metab 2010; 11: 347-68.
46. Khan MT. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr Drug Metab 2010; 11: 285-95.
47. Gonzalez-Diaz H, Duardo-Sanchez A, Ubeira FM, et al. 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.
48. Gonzalez-Diaz H. Network topological indices, drug metabolism, and distribution. Curr Drug Metab 2010; 11: 283-4.
49. Garcia I, Diop YF, Gomez G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr Drug Metab 2010; 11: 307-14.
50. Chou KC. Graphic rule for drug metabolism systems. Curr Drug Metab 2010; 11: 369-78.
51. Concu R, Podda G, Ubeira FM, 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 Design 2010; 16: 2710-23.
52. Estrada E, Molina E, Nodarse D, Uriarte E. Structural contributions of substrates to their binding to P-Glycoprotein. A TOPS-MODE approach. Curr Pharm Design 2010; 16: 2676-709.
53. Garcia I, Fall Y, Gomez G. QSAR, docking, and CoMFA studies of GSK3 inhibitors. Curr Pharm Design 2010; 16: 2666-75.
54. Gonzalez-Diaz H. QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neurosciences. Curr Pharm Design 2010; 16: 2598-600.
55. Gonzalez-Diaz H, Romaris F, Duardo-Sanchez A, et al. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr Pharm Design 2010; 16: 2737-64. amyloid precursor protein and presenilin 1 transgenes. Nat Med 1998; 4: 97-100.
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57. Cole SL, Vassar R. The basic biology of BACE1: a key therapeutic target for alzheimer's disease. Curr Genomics 2007; 8: 509-30.
58. Haass C, Schlossmacher MG, Hung AY, et al. Amyloid betapeptide is produced by cultured cells during normal metabolism. Nature 1992; 359: 322-5.
59. Seubert P, Oltersdorf T, Lee MG, et al. Secretion of beta-amyloid precursor protein cleaved at the amino terminus of the betaamyloid peptide. Nature 1993; 361: 260-3.
60. Fan Y, Unwalla R, Denny RA, et al. Insights for predicting bloodbrain barrier penetration of CNS targeted molecules using QSPR approaches. J Chem Inf Model 2010; 50: 1123-33.
61. Chou KC. Structural bioinformatics and its impact to biomedical science. Curr Med Chem 2004; 11: 2105-34.
62. Chou KC, Wei DQ, Du QS, Sirois S, Zhong WZ. Progress in computational approach to drug development against SARS. Curr Med Chem 2006; 13: 3263-70.
63. Todeschini R, Consonni V. Handbook of Molecular Descriptors. Wiley-VCH 2002.
64. Estrada E, Uriarte E. Recent advances on the role of topological indices in drug discovery research. Curr Med Chem 2001; 8: 1573-88.
65. González-Díaz H, González-Díaz Y, Santana L, Ubeira FM, Uriarte E. Proteomics, networks and connectivity indices. Proteomics 2008; 8: 750-78.
66. Nunez MB, Maguna FP, Okulik NB, Castro EA. QSAR modeling of the MAO inhibitory activity of xanthones derivatives. Bioorg Med Chem Lett 2004; 14: 5611-7.
67. Terada M, Inaba M, Yano Y, et al. Growth-inhibitory effect of a high glucose concentration on osteoblast-like cells. Bone 1998; 22: 17-23.
68. 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.
69. Marrero-Ponce Y. Linear indices of the "molecular pseudograph's atom adjacency matrix": definition, significance-interpretation, and application to QSAR analysis of flavone derivatives as HIV-1 integrase inhibitors. J Chem Inf Comput Sci 2004; 44: 2010-26.
70. 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: 1118-24.
71. Marrero-Ponce Y, Khan MT, Casanola Martin GM, et al. Prediction of Tyrosinase Inhibition Activity Using Atom-Based Bilinear Indices. ChemMedChem 2007; 2: 449-78.
72. 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.
73. Casanola-Martin GM, Marrero-Ponce Y, Khan MT, et al. Dragon method for finding novel tyrosinase inhibitors: Biosilico identification and experimental in vitro assays. Eur J Med Chem 2007; 42: 1370-81.
74. 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: 1580-605.
75. 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.
76. Gonzalez MP, Teran C, Saiz-Urra L, Teijeira M. Variable selection methods in QSAR: an overview. Curr Top Med Chem 2008; 8: 1606-27.
77. Gonzalez-Diaz H. Quantitative studies on Structure-Activity and Structure-Property Relationships (QSAR/QSPR). Curr Top Med Chem 2008; 8: 1554.
78. Gonzalez-Diaz H, Prado-Prado F, Ubeira FM. Predicting antimicrobial drugs and targets with the MARCH-INSIDE approach. Curr Top Med Chem 2008; 8: 1676-90.
79. 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.
80. Ivanciuc O. Weka machine learning for predicting the phospholipidosis inducing potential. Curr Top Med Chem 2008; 8: 1691-709.
81. 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.
82. Wang JF, Wei DQ, Chou KC. Drug candidates from traditional chinese medicines. Curr Top Med Chem 2008; 8: 1656-65.
83. Wang JF, Wei DQ, Chou KC. Pharmacogenomics and personalized use of drugs. Curr Top Med Chem 2008; 8: 1573-9.
84. Torrens F, Castellano G. Topological charge-transfer indices: from small molecules to proteins. Curr Proteomics 2009: 204-13.
85. Concu R, Dea-Ayuela MA, Perez-Montoto LG, et al. 3D entropy and moments prediction of enzyme classes and experimentaltheoretic study of peptide fingerprints in Leishmania parasites. Biochimica et Biophysica Acta 2009; 1794: 1784-94.
86. Ivanciuc O. Machine learning Quantitative Structure-Activity Relationships (QSAR) for peptides binding to human amphiphysin-1 SH3 domain. Curr Proteomics 2009; 4: 289-302.
87. Vilar S, Gonzalez-Diaz H, Santana L, Uriarte E. A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer. J Theor Biol 2009; 261: 449-58.
88. Giuliani A, Di Paola L, Setola R. Proteins as networks: a mesoscopic approach using haemoglobin molecule as case study. Curr Proteomics 2009; 6: 235-45.
89. Chou KC. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr Proteomics 2009; 6: 262-74.
90. Chen J, Shen B. Computational analysis of amino acid mutation: a proteome wide perspective. Curr Proteomics 2009; 6: 228-34.
91. Zhong WZ, 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.
92. Wang JF, Chou KC. Molecular modeling of cytochrome P450 and drug metabolism. Curr Drug Metab 2010; 11: 342-6.
93. Mrabet Y, Semmar N. Mathematical methods to analysis of topology, functional variability and evolution of metabolic systems based on different decomposition concepts. Curr Drug Metab 2010; 11: 315-41.
94. Martinez-Romero M, Vazquez-Naya JM, Rabunal JR, et al. Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network. Curr Drug Metab 2010; 11: 347-68.
95. Khan MT. Predictions of the ADMET properties of candidate drug molecules utilizing different QSAR/QSPR modelling approaches. Curr Drug Metab 2010; 11: 285-95.
96. Gonzalez-Diaz H, Duardo-Sanchez A, Ubeira FM, et al. 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.
97. Gonzalez-Diaz H. Network topological indices, drug metabolism, and distribution. Curr Drug Metab 2010; 11: 283-4.
98. Garcia I, Diop YF, Gomez G. QSAR & complex network study of the HMGR inhibitors structural diversity. Curr Drug Metab 2010; 11: 307-14.
99. Chou KC. Graphic rule for drug metabolism systems. Curr Drug Metab 2010; 11: 369-78.
100. Concu R, Podda G, Ubeira FM, 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 Design 2010; 16: 2710-23.
101. Estrada E, Molina E, Nodarse D, Uriarte E. Structural contributions of substrates to their binding to P-Glycoprotein. A TOPS-MODE approach. Curr Pharm Design 2010; 16: 2676-709.
102. Garcia I, Fall Y, Gomez G. QSAR, docking, and CoMFA studies of GSK3 inhibitors. Curr Pharm Design 2010; 16: 2666-75.
103. Gonzalez-Diaz H. QSAR and complex networks in pharmaceutical design, microbiology, parasitology, toxicology, cancer, and neurosciences. Curr Pharm Design 2010; 16: 2598-600.
104. Gonzalez-Diaz H, Romaris F, Duardo-Sanchez A, et al. Predicting drugs and proteins in parasite infections with topological indices of complex networks: theoretical backgrounds, applications, and legal issues. Curr Pharm Design 2010; 16: 2737-64.