Literature DB >> 21715466

Mining small-molecule screens to repurpose drugs.

S Joshua Swamidass1.   

Abstract

Repurposing and repositioning drugs--discovering new uses for existing and experimental medicines-is an attractive strategy for rescuing stalled pharmaceutical projects, finding treatments for neglected diseases, and reducing the time, cost and risk of drug development. As this strategy emerged, academic researchers began performing high-throughput screens (HTS) of small molecules--the type of experiments once exclusively conducted in industry--and making the data from these screens available to all. Several methods can mine this data to inform repurposing and repositioning efforts. Despite these methods' limitations, it is hopeful that they will accelerate the discovery of new uses for known drugs, but this hope has not yet been realized.

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Year:  2011        PMID: 21715466     DOI: 10.1093/bib/bbr028

Source DB:  PubMed          Journal:  Brief Bioinform        ISSN: 1467-5463            Impact factor:   11.622


  26 in total

Review 1.  Chemical biology applied to the study of bacterial pathogens.

Authors:  Rebecca Anthouard; Victor J DiRita
Journal:  Infect Immun       Date:  2014-11-17       Impact factor: 3.441

Review 2.  A survey of current trends in computational drug repositioning.

Authors:  Jiao Li; Si Zheng; Bin Chen; Atul J Butte; S Joshua Swamidass; Zhiyong Lu
Journal:  Brief Bioinform       Date:  2015-03-31       Impact factor: 11.622

Review 3.  Artificial intelligence unifies knowledge and actions in drug repositioning.

Authors:  Zheng Yin; Stephen T C Wong
Journal:  Emerg Top Life Sci       Date:  2021-12-21

4.  Computational Methods and Deep Learning for Elucidating Protein Interaction Networks.

Authors:  Dhvani Sandip Vora; Yogesh Kalakoti; Durai Sundar
Journal:  Methods Mol Biol       Date:  2023

5.  Challenges in secondary analysis of high throughput screening data.

Authors:  Aurora S Blucher; Shannon K McWeeney
Journal:  Pac Symp Biocomput       Date:  2014

Review 6.  Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines.

Authors:  Guangxu Jin; Stephen T C Wong
Journal:  Drug Discov Today       Date:  2013-11-14       Impact factor: 7.851

7.  Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.

Authors:  Matthew T Patrick; Kalpana Raja; Keylonnie Miller; Jason Sotzen; Johann E Gudjonsson; James T Elder; Lam C Tsoi
Journal:  J Invest Dermatol       Date:  2018-10-17       Impact factor: 8.551

Review 8.  Repurposing medicinal compounds for blood cancer treatment.

Authors:  Bronagh McCabe; Fabio Liberante; Ken I Mills
Journal:  Ann Hematol       Date:  2015-06-07       Impact factor: 3.673

Review 9.  Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

Authors:  Maryam Bagherian; Elyas Sabeti; Kai Wang; Maureen A Sartor; Zaneta Nikolovska-Coleska; Kayvan Najarian
Journal:  Brief Bioinform       Date:  2021-01-18       Impact factor: 11.622

10.  Drug-repurposing identified the combination of Trolox C and Cytisine for the treatment of type 2 diabetes.

Authors:  Ling Jin; Jian Tu; Jianwei Jia; Wenbin An; Huanran Tan; Qinghua Cui; Zhixin Li
Journal:  J Transl Med       Date:  2014-05-31       Impact factor: 5.531

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