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CMCL-DDI:用于药物相互作用预测的药物意识交叉视图对比学习
Yehong Han1, Lin Du1
1School of Information Science and Engineering, Qilu Normal University, Jinan, Shandong, China.
PloS one
|February 23, 2026
概括
预测药物相互作用 (DDI) 对药物安全至关重要. 这项研究引入了一个新的框架,CMCL-DDI,它结合了分子图和SMILES序列,以提高DDI预测的准确性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算化学计算化学
- 生物信息学是一种生物信息学.
背景情况:
- 准确预测药物相互作用 (DDI) 对于患者的安全和有效的药物治疗至关重要.
- 目前用于DDI预测的基于图形的方法往往忽视了简化分子输入线输入系统 (SMILES) 表示中存在的语义信息.
- 需要先进的计算方法来整合多样化的分子信息,以改善DDI预测.
研究的目的:
- 开发一种新的计算框架,CMCL-DDI,用于准确和可解释的药物相互作用预测.
- 为了利用药理意识分子图和SMILES序列来全面表示药物特性.
- 通过使用交叉视图相互对比学习和交叉注意力融合来增强DDI预测.
主要方法:
- 开发了CMCL-DDI,一个跨视图的相互对比学习框架.
- 用于功能分子特征提取和图表级嵌入的基于药基基的编码子图.
- 编码的SMILES序列,以捕获连续的药物特征.
- 采用对比式学习策略,在共享的潜伏空间中对准分子图和SMILES表示.
- 集成的异质特征使用交叉注意力融合模块进行强大的DDI预测.
主要成果:
- 与基准DDI预测数据集上现有的最先进模型相比,CMCL-DDI表现优越.
- 该框架有效地整合了来自分子图和SMILES序列的信息.
- 交叉视图表示学习显著提高了DDI预测的准确性和可解释性.
- 提出的方法强调了结合结构和序列药物信息的好处.
结论:
- 通过整合分子图和SMILES序列信息,CMCL-DDI提供了一种强大而有效的方法来预测药物相互作用.
- 交叉视图相互对比学习策略增强了代表性学习,以改善DDI预测.
- 这项工作强调了在计算型药物安全研究中利用互补数据模式的重要性.
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