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  • 标题:SmartGraph: a network pharmacology investigation platform
  • 本地全文:下载
  • 作者:Gergely Zahoránszky-Kőhalmi ; Timothy Sheils ; Tudor I. Oprea
  • 期刊名称:Journal of Cheminformatics
  • 印刷版ISSN:1758-2946
  • 电子版ISSN:1758-2946
  • 出版年度:2020
  • 卷号:12
  • 期号:1
  • 页码:1-11
  • DOI:10.1186/s13321-020-0409-9
  • 出版社:BioMed Central
  • 摘要:Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals. The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction. https://smartgraph.ncats.io/.
  • 关键词:Network pharmacology ; Pathway analysis ; Target deconvolution ; Network perturbation ; Protein–protein interactions (PPIs) ; Bioactivity prediction ; Potent chemical pattern ; Scaffold ; neo4j ; Network visualization ;
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