Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
General Research Article

MoABank: An Integrated Database for Drug Mode of Action Knowledge

Author(s): Yu-di Liao and Zhen-ran Jiang*

Volume 14, Issue 5, 2019

Page: [446 - 449] Pages: 4

DOI: 10.2174/1574893614666190416151344

Price: $65

Abstract

Background: With the declining trend of new drugs yield each year, more comprehensive knowledge of drug MoAs can help identify new applications of available drugs and discovery novel mechanism of drug action.

Objective: Therefore, construction of a specialized drug mode of action (MoA) database is of paramount importance for new drug research & development.

Methods: This paper introduces an integrated database for drug mode of action knowledge (MoABank).

Results: This database can provide the knowledge about drug MoAs, targets, pathways, side effects and other drug-related information for researchers.

Conclusion: We believe MoABank can make it more convenient for users to obtain the drug MoA information in the future.

Keywords: Information integration, MoA, database, drug molecular, cheminformatics, bioactivity data.

« Previous Next »
Graphical Abstract

[1]
Spratto GR, Woods AL. Delmar Nurse’s Drug Handbook. Cengage Learning 2010.
[2]
Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform 2016; 17(1): 2-12.
[3]
Napolitano F, Zhao Y, Moreira VM, et al. Drug repositioning: a machine-learning approach through data integration. J Cheminform 2013; 5(1): 30.
[4]
Medina-Franco JL, Giulianotti MA, Welmaker GS, Houghten RA. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov Today 2013; 18(9-10): 495-501.
[5]
Zampieri M. From the metabolic profiling of drug response to drug mode of action. Curr Opin Syst Biol 2018; 10: 26-33.
[6]
Law V, Knox C, Djoumbou Y, et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 2014; 42: D1091-7.
[7]
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017; 45(D1): D353-61.
[8]
Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res 2016; 44(D1): D1075-9.
[9]
Wang Y, Bryant SH, Cheng T, et al. PubChem BioAssay: 2017 update. Nucleic Acids Res 2017; 45(D1): D955-63.
[10]
Iorio F, Bosotti R, Scacheri E, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA 2010; 107(33): 14621-6.
[11]
Orton RJ, Sturm OE, Vyshemirsky V, Calder M, Gilbert DR, Kolch W. Computational modelling of the receptor-tyrosine-kinase-activated MAPK pathway. Biochem J 2005; 392(Pt 2): 249-61.
[12]
Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. BindingDB in 2015: A public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 2016; 44(D1): D1045-53.
[13]
Gaulton A, Bellis LJ, Bento AP, et al. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res 2012; 40: D1100-7.
[14]
Nijman SM. Functional genomics to uncover drug mechanism of action. Nat Chem Biol 2015; 11(12): 942-8.

Rights & Permissions Print Cite
Article Metrics
43
2
© 2024 Bentham Science Publishers | Privacy Policy