扩展GCN:基于多尺度相互作用融合机制和扩展的GCN的蛋白质-配体结合亲和力预测
Wei Wang1, Linchong Ma2, Dong Liu1
1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453000, China; Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province, Xinxiang 453000, China.
概括
这项研究介绍了AdptDilatedGCN,这是一种用于预测蛋白质-干结合亲和力的深度学习模型. 这种新的方法有效地整合了多级蛋白质和连接体特征,提高了药物发现的预测准确性.
科学领域:
- 计算化学是一种计算化学.
- 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:
- 准确预测蛋白质 - 配体结合亲和力对于有效的药物发现至关重要.
- 目前的方法很难充分利用蛋白质和连接体特征,并整合细粒和粗粒信息.
- 需要先进的计算模型来捕捉复杂的相互作用.
研究的目的:
- 开发一个深度学习模型,AdptDilatedGCN,用于增强蛋白质 - 配体结合亲和力预测.
- 解决现有方法在功能利用和信息整合方面的局限性.
- 提高绑定亲和预测的准确性和可靠性.
主要方法:
- 拟议的AdptDilatedGCN模型采用多尺度交互和融合机制.
- 使用了扩展图形卷积网络 (GCN),具有多头注意力和扩展卷积.
- 集成了一个适应性门式循环单元 (GRU),用于多源信息集成.
- 补充了GCN学习的功能,从AutoDock Vina.中使用Vina权重.
主要成果:
- 在CASF-2016数据集上获得0.837的皮尔森相关系数.
- 在CASF-2013数据集上获得0.803的皮尔森相关系数.
- 在捕获和整合细粒度和粗粒度信息方面表现出卓越的性能.
结论:
- 通过利用多尺度特征相互作用,AdptDilatedGCN有效地预测蛋白质-连接体结合亲和力.
- 该模型的架构增强了功能利用和集成能力.
- 这种方法为计算药物发现提供了有希望的进步.
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