使用TransR来增强COVID-19及其并发症的药物重定位知识图
Hongxi Zhao1, Hongfei Li2, Qiaoming Liu3
1College of Computer and Control Engineering, Northeast Forestry University, 150040, Harbin, HeiLongJiang, China.
Methods (San Diego, Calif.)
|December 17, 2023
概括
这项研究使用知识图重新定位了COVID-19治疗的现有药物. 该模型确定了15种有效药物,包括10种新型候选药物,加速了治疗开发.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 传染病 传染病 传染病
背景情况:
- 由于COVID-19的流行,迫切需要开发有效的治疗方法.
- 传统药物发现速度缓慢,突出了需要替代策略,如药物重用.
研究的目的:
- 开发一种计算模型,用于识别用于治疗COVID-19及其并发症的重用药物.
- 为了加速新兴传染病的药物发现管道.
主要方法:
- 构建了一个知识图,整合了来自DRUGBANK和GNBR的基因,药物和疾病信息.
- 雇员TransR知识表示学习和图形神经网络用于模型培训.
- 利用受过训练的模型来预测COVID-19治疗的潜在药物候选者.
主要成果:
- 从前30个预测中确定了15种治疗COVID-19及其并发症的高成功率的药物.
- 发现了10种新的候选药物,包括Torcetrapib和Tocopherol,在知识图中以前与COVID-19无关.
- 证明了该模型在药物开发中帮助医疗保健专业人员的潜力.
结论:
- 知识图和机器学习方法有效地识别了针对COVID-19的重用药物.
- 这种方法为紧急的健康危机提供了比传统药物开发更快的替代方案.
- 鉴定出的新型药物候选药物需要进一步调查COVID-19治疗.
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