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POLYPHARMA: A Polypharmacology Database |
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If you are interested in this database, please refer the following papers for further references. |
1.
Tan Z, Chen L, Zhang S. Comprehensive Modeling and Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting Kinase Inhibitor. Sci Rep.
. Sep 21;6:33534.(2016). 1.
Tan Z, Zhang S. Polypharmacology in Drug Development: A Minireview of Current Technologies. ChemMedChem
. Jun 20;11(12)(2016). 1.
Reddy
AS, Tan Z, Zhang S. Curation and analysis of multitargeting agents for polypharmacological modeling. J Chem Inf Model
. 54 (9) (2014). 1.
Reddy
AS, Zhang S. Polypharmacology:
drug discovery for the future. Expert Rev
Clin Pharmacol. 6(1), 41-47 (2013). 2.
Phatak
SS, Zhang S. A novel multi-modal drug
repurposing approach for identification of potent ack1 inhibitors. Pac Symp Biocomput. 29-40 (2013). 3.
Morrow
JK, Tian L, Zhang S. Molecular networks in drug
discovery. Crit Rev Biomed Eng. 38, 143-156 (2010). 4.
Yook
Sh, Oltvai Zn, Barabasi Al. Functional and topological characterization of
protein interaction networks. Proteomics
4(4), 928-942 (2004). 5.
Lipinski
C, Hopkins A. Navigating chemical space for biology and medicine. Nature 432(7019), 855-861 (2004). 6.
Hopkins
Al. Network pharmacology: the next paradigm in drug discovery. Nature Chemical Biology 4(11), 682-690
(2008). 7.
Hopkins
Al. Drug discovery: Predicting promiscuity. Nature
462(7270), 167-168 (2009). 8.
Apsel B, Blair Ja, Gonzalez B et al. Targeted
polypharmacology: discovery of dual inhibitors of
tyrosine and phosphoinositide kinases. Nature Chemical Biology 4(11), 691-699
(2008). 9.
Simon Z, Peragovics A, Vigh-Smeller M et al. Drug
effect prediction by polypharmacology-based
interaction profiling. J Chem Inf
Model 52(1), 134-145 (2012). 10. Brianso F, Carrascosa
Mc, Oprea Ti, Mestres J. Cross-Pharmacology Analysis of G Protein-Coupled
Receptors. Curr Top Med Chem
(2011). 11. Paolini Gv, Shapland Rh, Van Hoorn Wp, Mason Js, Hopkins Al. Global mapping of pharmacological space. Nat Biotechnol
24(7), 805-815 (2006). 12.
Yildirim
Ma, Goh Ki, Cusick Me, Barabasi Al, Vidal M. Drug-target network. Nat Biotechnol 25(10), 1119-1126 (2007). 13. Durrant
Jd, Amaro Re, Xie L et al. A multidimensional strategy to
detect polypharmacological targets in the absence of structural and sequence
homology. PLoS Comput Biol 6(1), e1000648 (2010 ). 14. Oprea Ti, Mestres
J. Drug Repurposing: Far Beyond New Targets for Old Drugs. Aaps J (2012). 15. Boran Adw, Iyengar R. Systems approaches to polypharmacology
and drug discovery. Current Opinion in
Drug Discovery & Development 13(3), 297-309 (2010). 16. Boran Adw, Iyengar R. Systems Pharmacology. Mount Sinai Journal of Medicine 77(4), 333-344 (2010). 17. Xie L, Xie
L, Kinnings Sl,
Bourne Pe. Novel Computational Approaches to Polypharmacology as a Means to Define Responses to
Individual Drugs. Annual Review of
Pharmacology and Toxicology, Vol 52 52, 361
(2012). 18.
Hopkins
Al. Network pharmacology. Nature
Biotechnology 25(10), 1110-1111 (2007). 19. Oprea
Ti, Nielsen Sk, Ursu O et al. Associating Drugs, Targets and
Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided
Drug Repurposing. Mol Inform 30(2-3), 100-111 (2011). 20. Achenbach J, Tiikkainen
P, Franke L, Proschak E.
Computational tools for polypharmacology and
repurposing. Future Medicinal Chemistry
3(8), 961-968 (2011). 21. 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 24(13), i232-240 (2008). 22. Dar Ac, Das Tk,
Shokat Km, Cagan Rl. Chemical genetic discovery of targets and anti-targets
for cancer polypharmacology. Nature 486(7401), 80-84 (2012). 23. Baumann
Kh, Du Bois A, Meier W et al. A phase II trial (AGO 2.11) in
platinum-resistant ovarian cancer: a randomized multicenter trial with sunitinib (SU11248) to evaluate dosage, schedule,
tolerability, toxicity and effectiveness of a multitargeted
receptor tyrosine kinase inhibitor monotherapy. Annals of Oncology 23(9), 2265-2271
(2012). 24. Winum Jy, Maresca A, Carta F, Scozzafava A, Supuran
Ct. Polypharmacology of sulfonamides: pazopanib, a multitargeted
receptor tyrosine kinase inhibitor in clinical use, potently inhibits several
mammalian carbonic anhydrases. Chemical
Communications 48(66), 8177-8179 (2012). 25. Schmid A, Blank Lm. Hypothesis-driven omics integration. Nature
Chemical Biology 6(7), 485-487 (2010). 26. Joyce Ar,
Palsson Bo. The model organism as a system:
integrating 'omics' data sets. Nature Reviews Molecular Cell Biology 7(3), 198-210 (2006). 27. Zhao S, Iyengar
R. Systems Pharmacology: Network Analysis to Identify Multiscale
Mechanisms of Drug Action. Annual Review
of Pharmacology and Toxicology, Vol 52 52,
505-521 (2012). 28. Kanehisa M, Goto
S, Sato Y, Furumichi M, Tanabe M. KEGG for
integration and interpretation of large-scale molecular data sets. Nucleic Acids Res 40(Database issue),
D109-114 (2012). 29. Kuhn M, Von Mering
C, Campillos M, Jensen Lj, Bork P. STITCH: interaction networks of chemicals and
proteins. Nucleic Acids Res
36(Database issue), D684-688 (2008). 30. Hecker N, Ahmed J, Von Eichborn J et al.
SuperTarget goes quantitative: update on drug-target
interactions. Nucleic Acids Res
40(Database issue), D1113-1117 (2012). 31. Sharman Jl,
Mpamhanga Cp, Spedding M et al.
IUPHAR-DB: new receptors and tools for easy searching and visualization of
pharmacological data. Nucleic Acids Res
39(Database issue), D534-538 (2011). 32. Olah M, Mracec
M, Ostopovici L
et al. WOMBAT: World of molecular bioactivity. In: Chemoinformatics in Drug Discovery. Oprea
Ti (Ed. Wiley-VCH, New York 223-239 (2004). 33. Keiser Mj,
Roth Bl, Armbruster Bn, Ernsberger P, Irwin Jj, Shoichet Bk. Relating protein
pharmacology by ligand chemistry. Nat Biotechnol 25(2), 197-206 (2007). 34. Lounkine E, Keiser Mj,
Whitebread S et
al. Large-scale prediction and testing of drug activity on side-effect
targets. Nature 486(7403), 361-367
(2012). 35. Barabasi Al, Oltvai
Zn. Network biology: understanding the cell's functional organization. Nat Rev Genet 5(2), 101-113 (2004). 36. Zhang Ah, Sun H, Yang B, Wang Xj. Predicting new molecular targets for rhein using network pharmacology. Bmc Systems Biology 6(2012). 37. Cheng F, Zhou Y, Li W, Liu G,
Tang Y. Prediction of chemical-protein interactions network with weighted
network-based inference method. PLoS One 7(7),
e41064 (2012). 38. Campillos M, Kuhn M, Gavin Ac, Jensen Lj, Bork P. Drug target identification using side-effect
similarity. Science 321(5886),
263-266 (2008). 39. Grinter Sz,
Liang Y, Huang Sy, Hyder Sm, Zou X. An inverse docking
approach for identifying new potential anti-cancer targets. J Mol Graph Model
29(6), 795-799 (2011). 40. Hui-Fang L, Qing S, Jian Z, Wei F. Evaluation of various inverse docking
schemes in multiple targets identification. J
Mol Graph Model 29(3), 326-330 (2010). 41. Chen Yz,
Zhi Dg. Ligand-protein inverse docking and its
potential use in the computer search of protein targets of a small molecule. Proteins 43(2), 217-226 (2001). 42. Abdulhameed Mdm, Chaudhury S, Singh N, Sun H, Wallqvist
A, Tawa Gj. Exploring Polypharmacology Using a ROCS-Based Target Fishing
Approach. Journal of Chemical Information
and Modeling 52(2), 492-505 (2012). 43. Davis Ap, Murphy Cg, Rosenstein Mc,
Wiegers Tc, Mattingly Cj. The Comparative Toxicogenomics
Database facilitates identification and understanding of chemical-gene-disease
associations: arsenic as a case study. BMC
Med Genomics 1, 48 (2008). 44. Hirschman L, Burns Ga, Krallinger M et al. Text mining for the biocuration workflow. Database
(Oxford) 2012, bas020 (2012). 45. Krallinger
M, Leitner F, Vazquez M et al. How to link ontologies and
protein-protein interactions to literature: text-mining approaches and the BioCreative experience. Database
(Oxford) 2012, bas017 (2012). 46. Dowell Kg, Mcandrews-Hill
Ms, Hill Dp,
Drabkin Hj, Blake Ja. Integrating text mining into the MGI biocuration workflow. Database
(Oxford) 2009, bap019 (2009). 47. Wiegers Tc,
Davis Ap, Cohen Kb, Hirschman L, Mattingly Cj. Text mining and manual curation
of chemical-gene-disease networks for the comparative toxicogenomics
database (CTD). BMC Bioinformatics
10, 326 (2009). 48. Kolb P, Ferreira Rs, Irwin Jj, Shoichet
Bk. Docking and chemoinformatic screens for new
ligands and targets. Curr Opin Biotechnol 20(4), 429-436 (2009). 49. Adams Jc,
Keiser Mj, Basuino L et al. A mapping of drug space from the
viewpoint of small molecule metabolism. PLoS Comput Biol 5(8), e1000474
(2009). 50. Wren Jd,
Bekeredjian R, Stewart Ja, Shohet Rv, Garner Hr. Knowledge discovery
by automated identification and ranking of implicit relationships. Bioinformatics 20(3), 389-398 (2004). 51. Cheng T, Li Q, Zhou Z, Wang Y,
Bryant Sh. Structure-based virtual screening for drug discovery: a
problem-centric review. Aaps J 14(1), 133-141 (2012). 52. Nidhi, Glick M, Davies Jw, Jenkins Jl. Prediction of biological targets for
compounds using multiple-category Bayesian models trained on chemogenomics databases. J Chem Inf
Model 46(3), 1124-1133 (2006). 53. Ravot E, Lisziewicz
J, Lori F. New uses for old drugs in HIV infection: the role of hydroxyurea, cyclosporin and
thalidomide. Drugs 58(6), 953-963
(1999). 54. Chong Cr, Sullivan Dj, Jr. New uses for old drugs. Nature 448(7154), 645-646 (2007). 55. Chen D, Dou Qp. New uses for old copper-binding drugs: converting
the pro-angiogenic copper to a specific cancer cell
death inducer. Expert Opin
Ther Targets 12(6), 739-748 (2008). 56. Sannella Ar, Casini A, Gabbiani C et al. New uses for old drugs. Auranofin, a clinically established antiarthritic
metallodrug, exhibits potent antimalarial effects in
vitro: Mechanistic and pharmacological implications. FEBS Lett 582(6), 844-847 (2008). 57. Vazquez-Martin A, Lopez-Bonetc E, Cufi S et al. Repositioning chloroquine and metformin to eliminate cancer stem cell
traits in pre-malignant lesions. Drug
Resist Updat 14(4-5), 212-223 (2010). 58. Allison M. NCATS launches drug
repurposing program. Nat
Biotechnol 30(7), 571-572 (2012). |
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