基于深度学习的复合物-目标相互作用预测模型的全面比较,揭示了指导设计原则
Sina Abdollahi1, Darius P Schaub1,2, Madalena Barroso3
1Institute of Medical Systems Biology, University Medical Center Hamburg-Eppendorf, Hamburg, 20251, Germany.
Journal of cheminformatics
|October 29, 2024
概括
对复合物-目标相互作用 (CTIs) 的深度学习模型进行了比较. DeepConv-DTI及其修改版Phys-DeepConv-DTI在预测药物标结合方面表现出卓越的表现.
科学领域:
- 计算化学和化学信息学
- 生物信息学和计算生物学
- 药物的发现和开发.
背景情况:
- 准确预测化合物-标相互作用 (CTIs) 对于有效的药物发现至关重要.
- 传统的实验查方法耗时且昂贵.
- 深度学习模型为CTI预测提供了一个有希望的替代方案,但需要进行全面的比较.
研究的目的:
- 进行12个最先进的深度学习模型的深入比较,用于CTI预测.
- 使用大量的,精心策划的CTI数据集建立一个基准.
- 确定最佳的模型架构和特征表示,以改进CTI预测.
主要方法:
- 策划了超过30万个具有约束力和非约束力的CTI数据集.
- 评估了12个具有多种蛋白质和化合物表示的深度学习架构.
- 建立了黄金标准数据集,用于可靠的模型性能比较.
- 开发并测试了一种经过修改的DeepConv-DTI模型,其中包含了物理化学特性.
主要成果:
- DeepConv-DTI在多个数据集中展示了优异的CTI预测性能,获得了高的MCC分数.
- DeepConv-DTI展示了高效的训练和推断时间.
- 将物理化学性质纳入DeepConv-DTI导致了最高的整体性能 (Phys-DeepConv-DTI).
结论:
- 可训练嵌入的基于卷积的穿越对于捕获蛋白质特征是有效的.
- 目标的物理化学特性显著提高了CTI预测模型的性能.
- 对功能和架构的系统评估为开发先进的CTI预测模型提供了路线图.
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