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深度对比学习方法用于药物向相互作用的预测
Jinlong Li1, Shusen Zhou1, Tong Liu2
1School of Computer and Artificial Intelligence, Ludong University, Yantai, China.
Computer methods in biomechanics and biomedical engineering
|February 26, 2026
概括
深度对比学习 (DeepCL) 提高了药物向相互作用 (DTI) 预测的准确性,特别是在数据有限的情况下. 这种新的框架通过解决数值问题和更好地分离相互作用对来提高预测.
科学领域:
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
- 机器学习 机器学习
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于有效的药物发现至关重要.
- 现有的方法经常在低覆盖率的数据集和数值稳定性方面扎.
研究的目的:
- 引入DeepCL,这是一个新的深度对比学习框架,用于改进DTI预测.
- 为了解决数值下流问题,并加强相互作用和非相互作用对之间的分离.
主要方法:
- 开发了DeepCL,这是一个使用ESM-2蛋白语言模型和摩根指纹的双路径框架.
- 实现了一个通用的sigmoid激活函数和基于边际的对比损失.
- 在共享的潜伏空间中对齐的异质蛋白质和分子特征.
主要成果:
- 在三个基准数据集 (戴维斯,BindingDB,BIOSNAP) 上,DeepCL显著超过了最先进的方法.
- 在标准和零射击DTI预测设置中实现了卓越的性能.
- 证明了AUPR和AUROC得分的改善,表明更高的预测准确性.
结论:
- DeepCL为DTI预测提供了一个强大,可扩展和数值稳定的解决方案.
- 该框架特别有望在数据不足的情况下加速药物发现.
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