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Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations

Overview of attention for article published in Journal of Cheminformatics, July 2016
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Title
Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations
Published in
Journal of Cheminformatics, July 2016
DOI 10.1186/s13321-016-0147-1
Pubmed ID
Authors

Santiago Vilar, George Hripcsak

Abstract

Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 3%
Portugal 1 3%
Germany 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Ph. D. Student 6 19%
Student > Master 5 16%
Student > Doctoral Student 3 10%
Lecturer 2 6%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 26%
Agricultural and Biological Sciences 6 19%
Chemistry 4 13%
Pharmacology, Toxicology and Pharmaceutical Science 3 10%
Computer Science 2 6%
Other 2 6%
Unknown 6 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 July 2016.
All research outputs
#10,650,930
of 12,010,397 outputs
Outputs from Journal of Cheminformatics
#461
of 467 outputs
Outputs of similar age
#222,274
of 267,920 outputs
Outputs of similar age from Journal of Cheminformatics
#9
of 10 outputs
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So far Altmetric has tracked 467 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.9. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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