PDGCL-DTI:平行双通道图对比学习用于在异质网络中的药物目标绑定预测
IEEE journal of biomedical and health informatics
|March 3, 2025
概括
本研究介绍了PDGCL-DTI,这是一种用于预测药物向相互作用 (DTI) 的新框架,可以使用图形对比学习有效处理数据不平衡和复杂网络. 它在DTI预测方面取得了卓越的准确性,帮助药物发现工作.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 预测药物向相互作用 (DTI) 对药物发现至关重要,但由于数据集不平衡和网络复杂性,它面临着挑战.
- 现有的DTI预测方法往往难以有效地整合异质信息.
研究的目的:
- 开发一种新的框架,PDGCL-DTI,用于对药物向相互作用的强大而准确的预测.
- 解决数据不平衡,并在DTI预测中利用本地和全球网络特征.
主要方法:
- PDGCL-DTI在异构的药物目标网络上使用双图对比学习方法.
- 一个AdaL-GCL模块动态调整样本重量以减轻数据不平衡.
- 结合了本地节点信息和全球结构信息,用于特征提取.
主要成果:
- 在ChEMBL,DrugBank和DAVIS数据集上,PDGCL-DTI在现有方法上表现出优越的性能.
- 在数据集中实现了0.958的平均AUC和0.95的平均准确性.
- 案例研究证实了对特定药物向相互作用的成功预测,突出显示了其实用性.
结论:
- PDGCL-DTI为DTI预测提供了强大的解决方案,有效处理数据不平衡和复杂的网络结构.
- 该框架集成本地和全球特征的能力提高了预测准确性和适应性.
- 通过改进的DTI预测,PDGCL-DTI显示了加速药物发现的巨大潜力.
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