使用注意神经网络预测药物目标亲和力
Xin Tang1, Xiujuan Lei1, Yuchen Zhang2
1School of Computer Science, Shaanxi Normal University, Xi'an 710119, China.
International journal of molecular sciences
|May 25, 2024
概括
这项研究引入了GRA-DTA,这是一个新的深度学习算法,用于预测药物向亲和力 (DTA). GRA-DTA显著提高了DTA预测的准确性,加速了药物发现.
科学领域:
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
- 在药理学中的机器学习.
背景情况:
- 药物向相互作用 (DTI) 在药物发现中至关重要.
- 实验性确定药物向亲和力 (DTA) 是耗时且昂贵的.
- 计算DTA预测对于有效的药物开发至关重要.
研究的目的:
- 提出一种新的深度学习算法,GRA-DTA,用于准确的药物向亲和力预测.
- 提高药物发现管道的效率和速度.
主要方法:
- 使用双向门式反复单元 (BiGRU),用于目标表示学习的软注意力.
- 用于药物表示学习的使用图表样本和汇总 (GraphSAGE).
- 综合药物和目标表示使用注意力神经网络 (ANN) 进行DTA预测.
主要成果:
- 在GRA-DTA中,平均平方误差为0.142 (KIBA) 和0.225 (戴维斯).
- 一致性指数达到0.897 (KIBA) 和0.890 (戴维斯).
- 在基准数据集上表现优于现有的先进的DTA预测算法.
结论:
- 在DTA预测方面,GRA-DTA表现出卓越的性能.
- 拟议的深度学习方法为加速药物发现提供了一个有前途的计算工具.
- GRA-DTA有效地区分药物和目标特征,以提高预测准确度.
关键词:
双向门式循环单元 (BiGRU) 是指双向门式循环单元.图表样本和汇总 (GraphSAGE) 图表样本和汇总注意神经网络 (ANN) 是一个注意神经网络.深度学习是一种深度学习.药物向相互作用 (DTI) 是一种药物向相互作用.更多相关视频
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