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使用多模式深度学习框架,准确预测抗氧化的结构-活性关系

Huynh Anh Duy^1,2, Tarapong Srisongkram³

¹Graduate School in the Program of Research and Development in Pharmaceuticals, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen, 40002, Thailand.

Journal of cheminformatics

|November 4, 2025

概括

我们开发了一个深度学习模型来预测功能性食品和化品的抗氧化 (AOP). 这种人工智能方法有效地识别出有前途的AOP候选人,加速药物发现.

科学领域:

生物化学和生物信息学
计算化学计算化学
酸科学酸科学

背景情况:

抗氧化 (AOPs) 对治疗氧化应激疾病具有前景,并在功能性食品和化品中有应用.
开发准确的AOP预测模型对于其高效的发现和应用至关重要.

研究的目的:

创建一个全面的定量结构-活动关系 (QSAR) 模型来预测AOP活动.
使用生成模型来设计新的AOP候选人.
为了提高AOP预测的准确性,稳定性和可解释性.

主要方法:

集成6个基于序列的结构表示与堆叠集团神经网络 (CNNs,BiLSTM,变压器).
采用一种生成模型用于新型AOP候选设计.
使用SHAP分析来确定模型的可解释性.

主要成果:

通过使用一次热编码的堆叠模型实现了高预测指标 (准确度,AUROC,AUPRC> 0.90;MCC> 0.80).
确定了影响抗氧化活性的关键氨基酸残留物 (阳性:Pro,Leu,Ala,Tyr,Gly;阴性:Met,Cys,Trp,Asn,Thr).
通过计算确定了604个高可信度的AOPs.

结论:

多式联网深度学习框架显著提高了AOP预测的准确性,稳定性和可解释性.
这种方法可以有效地发现具有高潜力的AOP.
提供了一个强大的管道,以加速在药物和功能应用中的发现.

关键词:

抗氧化剂类是一种抗氧化剂.生物信息学是一种生物信息学.深度学习是一种深度学习.多式联运框架多式联运框架结构与活动的关系.

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