MMRCL:一个可解释的多模式深度学习框架,用于预测hERG阻断器
Yang Su1, Jinzhou Wu2, Ao Yang3
1School of Computer Science and Engineering (School of Artificial Intelligence), Chongqing University of Science and Technology, Chongqing 401331, China.
Computational biology and chemistry
|February 3, 2026
概括
一个新的框架预测药物诱导的hERG通道抑制,致命的心脏问题的原因. 这种可解释的模型通过早期识别心脏毒性化合物来增强药物发现.
科学领域:
- 计算化学是一种计算化学.
- 心血管药理学心血管药理学
- 药物发现 药物发现
背景情况:
- 人类以太-a-go-go相关基因 (hERG) 编码了一个对心脏再极化至关重要的通道.
- 药物抑制hERG通道可能导致QT间隔延长,torsade de pointes和致命的心律失常.
- 早期识别hERG抑制剂对于药物开发至关重要,以预防心脏毒性,减少药物消耗,并最大限度地减少经济损失.
研究的目的:
- 开发一个可解释的多模态分子表示交叉学习框架 (MMRCL) 来准确预测hERG通道阻断剂.
- 整合多样化的分子特征,包括指纹和图表,以提高预测能力.
- 通过模型可解释性,为药物化学家提供可操作的见解.
主要方法:
- 开发了MMRCL,集成多维分子指纹和分子图.
- 采用双通道消息传递神经网络 (MPNN) 来实现原子和键级特征,以及用于指纹语义的多层感知器.
- 利用多头交叉注意力机制进行适应性特征融合,并使用完全连接的神经网络进行分类.
主要成果:
- 在内部和外部数据集上,MMRCL在七个最先进的模型中表现出卓越的表现.
- 在内部数据集上实现了高性能指标:AUC为0.8895,PRC为0.9073和MCC为0.6146.
- 解释性分析确定了与hERG阻断活性相关的关键毒性亚结构.
结论:
- MMRCL为识别hERG阻断剂提供了卓越的预测准确性和概括性.
- 该框架提高了模型的可解释性,有助于研究结构-活动关系.
- MMRCL为药物化学家提供了宝贵的见解,以减轻药物发现中的心脏毒性风险.
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