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MGCL-CAP:面具图对比学习与有门的交叉注意力用于化学过敏性预测

Jiahui Guan¹, Qianhui Jiang¹, Peilin Xie²

¹Division of Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China.

Journal of chemical information and modeling

|November 18, 2025

概括

预测化学过敏原对公共健康至关重要. 我们的新深度学习模型MGCL-CAP准确识别潜在的过敏原,改善安全评估和减少实验测试.

科学领域:

计算毒理学计算毒理学
化学信息学化学信息学
机器学习是机器学习.

背景情况:

消费和工业产品中的化学过敏原对公众健康构成风险.
传统的过敏原查方法缓慢且资源密集.
目前的计算方法由于捕捉分子复杂性的局限性而缺乏准确性.

研究的目的:

开发一个先进的深度学习框架,MGCL-CAP,用于准确的化学过敏性预测.
通过整合基于图形的学习和跨模式的注意力来改进现有的计算方法.
为高效的过敏原候选者优先排序和更安全的化学配方提供可靠的工具.

主要方法:

在图形同态网络编码器中利用掩盖图形对比学习.
采用随机子图掩盖来学习结构不变的图形嵌入.
集成的分子指纹与图形嵌入使用多头门交叉注意力模式的融合.

主要成果:

与最先进的过敏性预测剂相比,MGCL-CAP表现优越.
该模型在各种超参数设置中表现出稳定性.
解释性分析确定了与敏感化机制相关的子结构.

结论:

MGCL-CAP提供了一个强大的,准确的计算工具,用于化学过敏性评估.
该框架提高了优先考虑候选化学品和设计更安全的产品的效率.
从可解释性分析中获得的机制性见解支持未来的化学安全评估.

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