MSFCL:基于多源特征融合和对比学习的药物组合风险水平预测
Zhen-Ze Zhang1, Shao-Rong Chen1, Shen-Bao Yu2
1School of Computer and Information Engineering, Xiamen University of Technology, Xiamen 361024, China.
Journal of chemical information and modeling
|June 20, 2025
概括
这项研究引入了MSFCL,一种用于预测药物组合风险水平的新方法. MSFCL准确量化了风险区分,超过了对基准数据集的现有方法.
科学领域:
- 计算化学和生物信息学
- 药理学和药物安全问题
- 机器学习在医疗保健中的应用
背景情况:
- 准确的药物组合风险评估对于安全的临床实践至关重要.
- 现有的方法经常使用二进制分类,无法区分风险水平并处理不平衡的数据.
- 药物组合数据中的异质特征对语义对齐提出了挑战.
研究的目的:
- 开发一种可靠的方法来预测药物组合风险水平.
- 解决现有的二进制分类方法的局限性,包括数据不平衡和特征对齐.
- 建议MSFCL (多源特征融合和对比学习) 以提高风险预测.
主要方法:
- 将分子结构特征 (TrimNet) 与图形卷积网络集成为拓关系.
- 融合摩根指纹相似性与特征稳定性的先前约束.
- 采用自适应梯度噪声混合扰动来对不平衡数据进行对比学习.
- 利用多头注意力,剩余连接,标签光滑和焦点损失来对特征对齐和目标利.
主要成果:
- 在三个基准数据集上,MSFCL在所有评估指标上显著优于基线方法.
- 在DDInter数据集上,在准确性 (9.84%),宏F1 (14.97%),宏回忆 (11.91%) 和宏精度 (12.94%) 上取得了显著的改进.
- 在DrugBank和MDF-SA-DDI数据集上的多类分类任务中表现出卓越的概括能力.
结论:
- MSFCL为多类药物组合风险水平预测提供了有效的解决方案.
- 提出的方法成功地解决了数据不平衡和特征对齐问题.
- MSFCL为指导合理的临床药物治疗和提高药物安全提供了一个有前途的工具.
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