一种图形神经网络方法,用于预测人类微生物群中的药物易感性
Maryam1, Mobeen Ur Rehman2, Irfan Hussain2
1Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju, 54896, South Korea.
Computers in biology and medicine
|July 2, 2024
概括
这项研究引入了机器学习模型,以预测肠道微生物如何影响药物代谢. GINCOV模型准确地识别了易受微生物群枯竭的药物,有助于早期药物发现.
科学领域:
- 药理学 药理学是指药理学的学科.
- 微生物学 微生物学
- 生物信息学是一种生物信息学.
背景情况:
- 人类微生物组显著影响药物的疗效和新陈代谢.
- 胃肠道微生物群的相互作用可以使大约150种药物失活.
- 了解这些相互作用对于早期药物发现至关重要.
研究的目的:
- 开发药物易感性对肠道微生物群枯竭的预测模型.
- 探索机器学习在药物微生物组学中的应用.
主要方法:
- 使用基于图的神经网络模型 (GCN,GAT,GINCOV).
- 在药物结构 (SMILES) 和微生物群相互作用的数据集上训练模型.
- 分类药物易受肠道微生物群的耗尽.
主要成果:
- GINCOV模型在对药物敏感性进行分类时取得了93%的准确性.
- GINCOV的表现优于其他测试的图形神经网络模型.
- 证明了GNN在药物微生物组分析中的有效性.
结论:
- 图形神经网络模型,特别是GINCOV,为选受肠道微生物群影响的药物提供了一种有效的方法.
- 这种方法可以加速药物发现,并为个性化医疗策略提供信息.
- 促进了针对患者的药物剂量和配方的改进.
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