%0 Book Section
%A Passi, Anurag
%A Jolly, Bani
%A Sharma, Tina
%A Pandya, Ashma
%A Bhardwaj, Anshu
%B In Silico Drug Design: Repurposing Techniques and Methodologies 
%C USA
%D 2019
%E Roy, Kunal 
%F open:2419
%I Elsevier Science
%K Antimicrobial resistanceDrug repurposingSystems biologyNetwork analysisDrug targetBig dataChemoinformaticsStructural proteomeDruggable genomeInfectious diseasesPriority pathogens
%P 229-253
%T Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens
%U http://crdd.osdd.net/open/2419/
%X 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.    
%Z Copyright of this article belongs to Elsevier Science.