A New Database for Drug Discovery Through Application of Data-Integration and Semantics

Author(s):  
Ole Kristian Ekseth ◽  
Jan Christian Meyer ◽  
Svein Olaf Hvasshovd
2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.


2012 ◽  
Vol 23 (4) ◽  
pp. 609-616 ◽  
Author(s):  
Murat Iskar ◽  
Georg Zeller ◽  
Xing-Ming Zhao ◽  
Vera van Noort ◽  
Peer Bork

Author(s):  
Lionel Urán Landaburu ◽  
Ariel J Berenstein ◽  
Santiago Videla ◽  
Parag Maru ◽  
Dhanasekaran Shanmugam ◽  
...  

Abstract The volume of biological, chemical and functional data deposited in the public domain is growing rapidly, thanks to next generation sequencing and highly-automated screening technologies. These datasets represent invaluable resources for drug discovery, particularly for less studied neglected disease pathogens. To leverage these datasets, smart and intensive data integration is required to guide computational inferences across diverse organisms. The TDR Targets chemogenomics resource integrates genomic data from human pathogens and model organisms along with information on bioactive compounds and their annotated activities. This report highlights the latest updates on the available data and functionality in TDR Targets 6. Based on chemogenomic network models providing links between inhibitors and targets, the database now incorporates network-driven target prioritizations, and novel visualizations of network subgraphs displaying chemical- and target-similarity neighborhoods along with associated target-compound bioactivity links. Available data can be browsed and queried through a new user interface, that allow users to perform prioritizations of protein targets and chemical inhibitors. As such, TDR Targets now facilitates the investigation of drug repurposing against pathogen targets, which can potentially help in identifying candidate targets for bioactive compounds with previously unknown targets. TDR Targets is available at https://tdrtargets.org.


2014 ◽  
Vol 50 ◽  
pp. 90
Author(s):  
B. Bernard ◽  
M. Miller ◽  
H. Rovira ◽  
I. Shmulevich

2018 ◽  
Vol 13 (9) ◽  
pp. 791-794 ◽  
Author(s):  
Theodora Katsila ◽  
Minos-Timotheos Matsoukas

2005 ◽  
Vol 4 (1) ◽  
pp. 45-58 ◽  
Author(s):  
David B. Searls

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