分离的双颗粒度再平衡的金字塔网络用于药物向相互作用预测
Zhiyuan Dong1, Yijia Zhang2, Yang Shi3
1School of Computer and Artificial Intelligence, Zhengzhou University, No. 100 Science Avenue, Zhengzhou 450000, China.
我们开发了DDGR-DTI,这是一种用于药物向相互作用 (DTI) 预测的新框架. 它准确地识别了结合点,并通过平衡数据来改进多式联接,优于现有方法.
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.
- 药物发现 药物发现
背景情况:
- 药物向相互作用 (DTI) 的预测对于药物发现至关重要,但由于药物和蛋白质之间的尺寸差异,它面临着挑战,阻碍了准确的结合部位预测.
- 学习偏差中的模式不平衡可能会破坏多式模式表示在DTI预测中的有效性.
研究的目的:
- 提出DDGR-DTI,这是一个新的框架,解决了DTI预测方面的挑战,特别是具有约束力的地点识别和模式不平衡.
- 提高DTI预测模型的准确性和概括能力.
主要方法:
- 拟议的DDGR-DTI使用了脱的双颗粒度框架,将DTI任务分为宏观 (基于模式的子任务) 和微观 (在子任务中的表示) 层面.
- 双流注意模块用于细粒度的基结构层次相互作用,以准确识别结合点.
- 再平衡金字塔网络 (RPN) 用于通过层次聚合来缓解多式联络融合中的模式不平衡.
主要成果:
- 在基准测试中,DDGR-DTI与现有的最先进模型相比表现优越.
- 该模型显示在药物向相互作用预测中增强了预测性能和概括能力.
- 该框架有效地解决了与具有约束力的地点识别和模式不平衡相关的挑战.
结论:
- 通过有效处理大小差异和模式不平衡,DDGR-DTI在药物向相互作用预测方面取得了重大进展.
- 拟议的框架提高了DTI预测的准确性和可靠性,促进了更有效的药物发现管道.
- 源代码和数据集是公开的,促进了该领域的进一步研究和开发.
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