转化生物医学

  • 国际标准期刊号: 2172-0479
  • 期刊 h 指数: 16
  • 期刊引用分数: 5.91
  • 期刊影响因子: 3.66
索引于
  • 打开 J 门
  • Genamics 期刊搜索
  • 期刊目录
  • 研究圣经
  • 全球影响因子 (GIF)
  • 中国知网(CNKI)
  • 引用因子
  • 西马戈
  • 电子期刊图书馆
  • 研究期刊索引目录 (DRJI)
  • OCLC-WorldCat
  • 普罗奎斯特传票
  • 普布隆斯
  • 米亚尔
  • 大学教育资助委员会
  • 日内瓦医学教育与研究基金会
  • 谷歌学术
  • 夏尔巴罗密欧
  • 秘密搜索引擎实验室
  • 研究之门
分享此页面

抽象的

Artificial intelligence in COVID-19 drug repurposing

Iman Beheshti

Drug repurposing or repositioning may be a technique whereby existing drugs are wont to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach due to the chance for reduced development timelines and overall costs. The artificial intelligence (AI) pioneers of the 1950s foresaw building machines that would sense, reason, and think like people—a proof-of-concept referred to as general AI. The increasing cost of drug development is thanks to the massive volume of compounds to be tested in preclinical stages and therefore the high proportion of randomised controlled trials (RCTs) that don't find clinical benefits or with toxicity issues. This Review provides a robust rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic. Drug repurposing may be a convenient alternative when the necessity for brand spanking new drugs in an unexpected medical scenario is urgent, as is that the case of emerging pathogens. In recent years, approaches supported network biology have demonstrated to be superior to gene-centric ones. Mechanistic models of pathways provide a natural bridge from variations at the size of gene activity (transcription) to variations in phenotype (at the extent of cells, tissues, or organisms). Interestingly, the notion of causality provided by the mechanistic model of the COVID-19 disease map are often exploited beyond the own pathways modeled.

免责声明: 此摘要通过人工智能工具翻译,尚未经过审核或验证