抗病毒DL:使用图形神经网络和自我监督学习的计算抗病毒药物重新定位
IEEE journal of biomedical and health informatics
|November 3, 2023
概括
新的计算框架AntiViralDL使用自主监督学习来更有效地预测抗病毒药物. 这种方法通过识别潜在的病毒-药物关联来增强药物发现,优于现有的方法.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 病毒感染给全球健康带来了重大挑战.
- 传统的抗病毒药物开发是资源密集和低效的.
- 计算方法为加速药物发现提供了一个有希望的替代方案.
研究的目的:
- 开发一个高效的计算框架,AntiViralDL,用于预测病毒与药物之间的关联.
- 利用自我监督学习和图形卷积网络来提高预测准确度.
- 为了解决病毒与药物相关性预测中的数据稀疏性.
主要方法:
- 通过整合Drugvirus2和FDA批准的数据,构建了一个病毒-药物关联数据集.
- 雇员光图卷积网络 (LightGCN) 用于学习病毒和药物嵌入.
- 利用对比式学习和随机噪声数据增强来改善预测.
- 使用内部产物计算预测病毒与药物相关性.
主要成果:
- 抗病毒DL的AUC达到了0.8450和AUPR达到了0.8494.
- 超过了四个基准病毒与药物关联预测模型.
- 通过一个案例研究,在识别潜在的抗COVID-19候选药物方面证明了有效性.
结论:
- 抗病毒DL提供了一种高效和准确的计算方法来预测抗病毒药物.
- 该框架有效地解决了数据稀疏性,并改善了预测性能.
- 抗病毒DL显示出加速发现新型抗病毒疗法的巨大潜力.
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