UAMRL:多粒度不确定性意识的多模式表示学习,用于药物向 afinity 预测
Wenzhe Xu1, Xiaorong Liu2, Jie Wang3
1School of Computer and Software Engineering, Xihua University, Chengdu 610039, China.
Bioinformatics (Oxford, England)
|September 30, 2025
概括
这项研究引入了一个不确定性意识的多模式表示学习 (UAMRL) 框架,用于药物向亲和力 (DTA) 预测. 通过整合多式联运数据和量化不确定性,UAMRL提高了DTA预测的准确性和透明度.
科学领域:
- 计算化学是一种计算化学.
- 药理学 药理学是指药理学的学科.
- 机器学习在药物发现中的作用
背景情况:
- 药物标亲和力 (DTA) 的计算预测对于药物发现至关重要.
- 深度学习模型在解释性和处理异构的多式联络数据方面面临挑战.
研究的目的:
- 为可靠和可解释的DTA预测开发一个新的框架.
- 解决现有的深度学习模型在处理多式联网数据方面的局限性.
主要方法:
- 提出了一个不确定性意识的多模式表示学习 (UAMRL) 框架.
- 使用双流编码器进行交叉模式的协会学习.
- 集成了一个不确定性量化机制,使用正常-反向-马分布.
主要成果:
- 在多个DTA数据集上,UAMRL实现了卓越的预测准确性.
- 该框架显示了预测性能的改善.
- 在药物开发申请中提高决策透明度.
结论:
- 通过有效整合多式联运数据和量化不确定性,UAMRL为DTA预测提供了可靠的方法.
- 该框架提高了预测准确性和可解释性,有助于实用的药物开发.
- 拟议的不确定性量化机制提高了数据融合的稳定性.
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