MFD-GDrug:基于多模式功能融合的深度学习,用于GPCR-药物相互作用预测
Xingyue Gu1, Junkai Liu2, Yue Yu3
1State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China.
Methods (San Diego, Calif.)
|January 29, 2024
概括
新的多式联机深度学习模型MFD-GDrug准确地预测了涉及G蛋白结合受体 (GPCR) 的药物蛋白相互作用 (DPI). 这种方法通过为实验验证提供一个具有成本效益和精确的替代方案来增强药物发现.
科学领域:
- 药理学和生物信息学 药理学和生物信息学
- 计算机化药物发现技术
- 机器学习在医学中的应用
背景情况:
- 准确识别药物蛋白相互作用 (DPI) 对于有效的药物开发至关重要.
- G蛋白结合受体 (GPCR) 是药物发现的关键目标,但GPCR与药物相互作用的实验验证是资源密集的.
- 需要计算方法来准确和经济有效地预测GPCR-药物配对.
研究的目的:
- 开发一种新的多式联络深度学习模型,MFD-GDrug,专门用于预测GPCRs的药物蛋白相互作用 (DPI).
- 整合药物和蛋白质的多样化分子特征,以提高预测性能.
- 提供一个计算高效和准确的工具,以帮助GPCR向药物发现.
主要方法:
- 使用ESM预训练模型提取蛋白质序列特征.
- 使用卷积神经网络 (CNN) 来表示蛋白质特征.
- 集成的多模式药物特征,包括3D分子信息 (Mol2vec) 和图形拓数据 (图形卷积网络 - GCN).
- 开发了一种多式深度学习架构 (MFD-GDrug),将蛋白质和药物特征结合起来进行相互作用预测.
主要成果:
- 与基准GPCR药物相互作用数据集上的现有方法相比,MFD-GDrug显示出更高的预测准确性.
- 该模型有效地通过整合多模式蛋白质和药物特征来捕捉复杂的相互作用.
- 预训练的嵌入和结构特征的组合在预测GPCR-药物配对方面被证明是有效的.
结论:
- MFD-GDrug提供了一种高度准确和高效的计算方法,用于预测涉及GPCRs的药物蛋白相互作用.
- 多式联络深度学习策略有效地利用各种分子数据来改善预测.
- 这种模型可以通过减少对GPCR药物标的昂贵实验验证的依赖,显著加速药物发现管道.
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