解决数据偏差可以提高结合性亲和力预测中的概括性
David Graber1,2,3, Peter Stockinger2,4, Fabian Meyer2
1Seminar for Applied Mathematics, Department of Mathematics and ETH AI Center, Zurich, Switzerland.
Nature machine intelligence
|October 27, 2025
概括
数据泄露在蛋白质 - 配体结合亲和力预测膨胀模型性能. 我们的PDBbind CleanSplit数据集和图形神经网络模型揭示了真正的概括能力,解决了计算药物设计中的关键问题.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 机器学习是机器学习.
背景情况:
- 准确预测蛋白质 - 配体结合亲缘关系对于计算药物设计至关重要.
- 现有的深度学习模型通常会显示由于PDBbind数据库和基准数据集的训练测试数据泄露而导致性能膨胀.
- 这种漏洞高估了当前绑定亲和力预测模型的概括能力.
研究的目的:
- 为了解决列车测试数据泄露在绑定亲和力预测数据集中的问题.
- 开发一个可靠的基准数据集和一个强大的模型来评估概括能力.
- 在计算药物设计中识别深度学习模型的真实性能.
主要方法:
- 开发了PDBbind CleanSplit,这是一个精心策划的训练数据集,使用一种基于结构的新过算法来消除数据泄露和冗余.
- 在CleanSplit数据集上重新训练现有的高性能深度学习模型.
- 开发了一种新的图形神经网络模型,利用稀疏图形建模蛋白质 - 配体相互作用,并从语言模型中转移学习.
主要成果:
- 在CleanSplit上重新训练现有模型导致性能大幅下降,证实了数据泄露的重大影响.
- 拟议的图形神经网络模型在CleanSplit基准上保持了高性能.
- 图形神经网络模型在严格独立的测试数据集上展示了强大的概括能力.
结论:
- 由于数据泄露,许多当前的深度学习模型对绑定亲和力预测的性能在很大程度上被高估了.
- PDBbind CleanSplit 数据集提供了对模型概括的更现实的评估.
- 开发的图形神经网络模型为药物设计中准确和可概括的结合亲和力预测提供了一个有希望的方法.
相关概念视频
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