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Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens
 
Creator Passi, Anurag
Jolly, Bani
Sharma, Tina
Pandya, Ashma
Bhardwaj, Anshu
 
Subject QR Microbiology
 
Description Antimicrobial resistance (AMR) is the outcome of genotypic and phenotypic diversification of lineages that is rapidly increasing among various pathogenic strains globally. In 2017 the World Health Organization (WHO) released a list of priority pathogens that can only be tackled with the discovery of novel antibiotics. New drug discovery and development entails high costs and attrition rates and therefore new strategies to address AMR are urgently needed. With the accumulation of large volumes of pharmacological and “-omics” data and strong analytical tools, in silico approaches play an increasingly important role in the discovery of novel antibiotics as well as repurposing of known drugs for new indications. This chapter discusses the available high-throughput chemical-biology integrative data platforms including network-based approaches for drug repurposing. The chapter also discusses contemporary and futuristic methods/resources for exploring and extending the existing targets and chemical space with semantic linked data technologies and deep-learning methods that are being adapted for drug repurposing.
 
Publisher Elsevier Science
 
Contributor Roy, Kunal
 
Date 2019
 
Type Book Section
PeerReviewed
 
Identifier Passi, Anurag and Jolly, Bani and Sharma, Tina and Pandya, Ashma and Bhardwaj, Anshu (2019) Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens. In: In Silico Drug Design: Repurposing Techniques and Methodologies. Elsevier Science, USA, pp. 229-253. ISBN 978-0-12-816125-8
 
Relation http://crdd.osdd.net/open/2419/