一种可解释的分子标记估计方法,用于知识意识的药物相互作用预测
IEEE journal of biomedical and health informatics
|January 21, 2026
概括
本研究探讨了用于药物相互作用 (DDI) 预测的分子表示学习 (MRL). 一种新的方法,SimMotifPro,利用动机令牌来提高模型性能和对分子嵌入的理解.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 生物信息学是一种生物信息学.
背景情况:
- 分子表示学习 (MRL) 使用像原子和图案这样的令牌来表示图形神经网络 (GNN) 的分子.
- 虽然使用代币的GNN在药物相互作用 (DDI) 预测方面表现有前途,但代币选择对分子嵌入表现力的影响尚不清楚.
研究的目的:
- 从频率域的角度理论上定义MRL,并分析符号计数对模型性能的影响.
- 提出SimMotifPro,一个高效的基于动机的方法,用于DDI预测,并结合了理论见解.
主要方法:
- 从频率域的角度开发了MRL的公理定义,以建立模型融合的理论上限.
- 提出了SimMotifPro,使用DeeperGCN编码器,动机-动机知识图和动机排名模块来增强DDI预测.
主要成果:
- 证明SimMotifPro与衍生的理论上限保持一致,验证了拟议理论的一般适用性.
- 在多个DDI预测基准上取得了最先进的表现,展示了基于动机的方法的有效性.
结论:
- 代币的数量显著影响MRL模型的性能,理论见解指导开发有效的分子嵌入策略.
- SimMotifPro为DDI预测提供了强大而高效的解决方案,突出了基于图案的表示和先进的GNN架构的重要性.
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