ContraDTI:通过多视图对比学习改进了药物向相互作用预测
Zhirui Liao1, Lei Xie2, Shanfeng Zhu3
1School of Physics and Electronic Information, Guangxi Minzu University, 188 Daxuedong Rd., Nanning, 530006, China; Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University, 220 Handan Rd., Shanghai, 200433, China; Guangxi Colleges and Universities Engineering Research Center for Multi-Modal Information Intelligent Sensing, Processing and Application, 188 Daxuedong Rd., Nanning, 530006, China.
ContraDTI是一个新的框架,使用多视图对比学习来预测药物向相互作用. 它有效地克服了数据限制,提高了DTI预测性能,在稀缺标记数据的场景中.
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.
- 机器学习 机器学习
背景情况:
- 药物向相互作用 (DTI) 的识别对于药物发现至关重要.
- 机器学习加速了DTI预测,但受到稀缺的注释数据的限制.
- 现有的监督方法在有限的标记药物数据下扎.
研究的目的:
- 引入ContraDTI,这是使用多视图对比学习进行DTI预测的新框架.
- 为了应对DTI预测中有限的标记数据的挑战.
- 在数据有限的场景中提高DTI预测模型的性能.
主要方法:
- ContraDTI采用多视图对比式学习方法.
- 它使用药物的分子图表作为主要视图,其SMILES字符串作为侧面视图.
- 两个损失函数用于主视图对比度和交叉视图对齐.
主要成果:
- ContraDTI显著提高了DTI预测分类的性能.
- 该框架表现出卓越的性能,特别是在低数据体系中.
- 对单目标和多目标DTI数据集的实验验验证了它的有效性.
结论:
- ContraDTI是用于药物向相互作用预测的强大工具.
- 它有效地克服了DTI预测中的数据稀缺性挑战.
- 该框架为数据有限的药物发现场景提供了一个有希望的解决方案.
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