FedKD-CPI:结合联合知识蒸技术来实现协同化合物-蛋白质相互作用预测的协同作用
Xuetao Wang1, Qichang Zhao1, Jianxin Wang1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China; Hunan Provincial Key Lab on Bioinformatics, Central South University, Changsha 410083, China.
Methods (San Diego, Calif.)
|January 17, 2025
概括
本研究介绍了FedKD-CPI,这是一个用于预测化合物-蛋白相互作用 (CPI) 的新框架. 它加强了药物发现的多机构合作,同时确保数据隐私和安全.
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:
- 化合物-蛋白相互作用 (CPI) 的预测对于加速药物发现至关重要.
- 数据隐私和知识产权限制了多机构在CPI预测方面的数据共享.
- 现有的方法难以平衡协作预测准确性和数据安全性.
研究的目的:
- 提出FedKD-CPI,这是第一个利用联合知识蒸用于协作CPI预测的框架.
- 加强在CPI预测中的多方合作,同时确保数据隐私和安全.
- 通过安全的知识聚合,提高CPI预测的准确性.
主要方法:
- 开发了FedKD-CPI,这是一个结合的学习框架,包含知识蒸.
- 采用知识蒸,从中央服务器上的客户端模型中提取和汇总知识.
- 利用公共和私人数据进行培训,以最大限度地提取知识.
主要成果:
- FedKD-CPI实现了与集中学习相美的预测准确性.
- 该框架显著优于本地化的学习方法.
- 在各种数据分布上,FedKD-CPI在现有的联合学习基线上表现优越.
结论:
- FedKD-CPI有效地提高了化合物-蛋白相互作用预测的准确性.
- 该框架在多机构合作中确保数据安全和隐私.
- 通过促进安全的协作和高效的知识利用,FedKD-CPI加速了药物发现.
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