トランスレーショナル生物医学

  • ISSN: 2172-0479
  • ジャーナル h-index: 16
  • 雑誌引用スコア: 5.91
  • ジャーナルのインパクトファクター: 3.66
インデックス付き
  • Jゲートを開く
  • Genamics JournalSeek
  • ジャーナル目次
  • 研究聖書
  • グローバル インパクト ファクター (GIF)
  • 中国国家知識基盤 (CNKI)
  • サイテファクター
  • シマゴ
  • 電子ジャーナルライブラリ
  • 研究ジャーナル索引作成ディレクトリ (DRJI)
  • OCLC-WorldCat
  • プロクエスト召喚
  • パブロン
  • ミアル
  • 大学補助金委員会
  • ジュネーブ医学教育研究財団
  • Google スカラー
  • シェルパ・ロメオ
  • 秘密検索エンジン研究所
  • リサーチゲート
このページをシェアする

抽象的な

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.