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多AOP:一种轻量级的多视图深度学习框架,用于抗氧化发现
Jianxiu Cai1,2, Xinpo Lou1,3, Chak Fong Chong1,2
1Faculty of Applied Sciences, Macao Polytechnic University, Rua de Luís Gonzaga Gomes, Macau SAR, China.
Bioresources and bioprocessing
|February 2, 2026
概括
发现抗氧化 (AOP) 对健康和食品保存至关重要. 一个新的深度学习框架,Multi-AOP,有效地使用序列和图形数据识别AOP,优于现有的方法.
科学领域:
- 生物化学和生物信息学
- 计算化学计算化学
- 人工智能在药物发现中的作用
背景情况:
- 抗氧化 (AOPs) 显示出疾病预防和食品保存的潜力,因为它们的自由基清除特性.
- 发现AOP的传统实验方法效率低下,资源密集.
研究的目的:
- 为增强抗氧化剂的发现开发一个高效的计算框架.
- 整合序列和结构信息,以提高AOP预测的准确性.
主要方法:
- 开发了Multi-AOP,一个轻量级的多视图深度学习框架,使用扩展长期短期内存 (xLSTM) 进行序列嵌入.
- 在SMILES表示上使用传递信息的神经网络 (MPNN) 来提取分子图的特征,捕获物理化学性质.
- 实现了序列和图形特征的层次融合,用于全面的类分析.
主要成果:
- 多AOP实现了高的预测准确度:0.8043 (AnOxPePred),0.9684 (AnOxPP) 和0.9043 (AOPP). 通过多AOP实现了高的预测准确度:0.8043 (AnOxPePred),0.9684 (AnOxPP),以及0.9043 (AOPP).
- 该框架始终优于传统的机器学习和最先进的深度学习方法.
- 创建了一个统一的AOP数据集,以促进可概括的AOP预测模型的开发.
结论:
- 多AOP框架在有效和准确的抗氧化发现方面取得了重大进展.
- 综合序列和图形学习为预测功能提供了强大的方法.
- 公共可访问的数据集和模型将加速AOP开发的未来研究.
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