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Identifying enriched drug fragments as possible candidates for metabolic engineering

Overview of attention for article published in BMC Medical Genomics, August 2016
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Title
Identifying enriched drug fragments as possible candidates for metabolic engineering
Published in
BMC Medical Genomics, August 2016
DOI 10.1186/s12920-016-0205-6
Pubmed ID
Authors

Sunandini Sharma, Kritika Karri, Ishwor Thapa, Dhundy Bastola, Dario Ghersi

Abstract

Fragment-based approaches have now become an important component of the drug discovery process. At the same time, pharmaceutical chemists are more often turning to the natural world and its extremely large and diverse collection of natural compounds to discover new leads that can potentially be turned into drugs. In this study we introduce and discuss a computational pipeline to automatically extract statistically overrepresented chemical fragments in therapeutic classes, and search for similar fragments in a large database of natural products. By systematically identifying enriched fragments in therapeutic groups, we are able to extract and focus on few fragments that are likely to be active or structurally important. We show that several therapeutic classes (including antibacterial, antineoplastic, and drugs active on the cardiovascular system, among others) have enriched fragments that are also found in many natural compounds. Further, our method is able to detect fragments shared by a drug and a natural product even when the global similarity between the two molecules is generally low. A further development of this computational pipeline is to help predict putative therapeutic activities of natural compounds, and to help identify novel leads for drug discovery.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 4%
Unknown 24 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 24%
Student > Master 4 16%
Researcher 3 12%
Professor 2 8%
Student > Doctoral Student 2 8%
Other 4 16%
Unknown 4 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 7 28%
Biochemistry, Genetics and Molecular Biology 3 12%
Pharmacology, Toxicology and Pharmaceutical Science 2 8%
Chemistry 2 8%
Chemical Engineering 1 4%
Other 4 16%
Unknown 6 24%