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AOP-DRL:一种深度表示学习框架,用于抗氧化的计算预测

Yongzhu Zhou¹, Wanlin Liu², Qiao Liu¹

¹School of Medicine, Hunan Normal University, No. 36, Lushan Road, Changsha, Hunan 410081, China.

Computational and structural biotechnology journal

|January 16, 2026

概括

我们开发了一个用于预测抗氧化的深度学习模型,为传统方法提供了一个可扩展和具有成本效益的替代方案. 这种人工智能方法加速了用于各种应用的治疗和营养素的发现.

科学领域:

生物化学生物化学
生物信息学是一种生物信息学.
人工智能的人工智能

背景情况:

分子氧产生反应性氧物种 (ROS),导致氧化应激和细胞损伤.
抗氧化可以对抗ROS,但由于可扩展性问题,使用传统方法难以识别.

研究的目的:

开发一种高吞吐量,可扩展和具有成本效益的深度学习框架,用于预测抗氧化.
克服当前用于识别抗氧化的方法的局限性,特别是关于可变长度和复杂残留相互作用的现有方法.

主要方法:

开发了抗氧化深度表示学习 (AOP-DRL),这是一个深度学习框架,集成蛋白质语言模型和层次卷积网络.
利用实验验证的抗氧化剂序列和来自公共存储库的负控制的训练数据集.
采用标准的氧化还原蛋白质学数据分区协议,以进行可靠的模型评估.

主要成果:

与不同数据集的最先进模型相比,AOP-DRL表现出更高的准确性和概括能力.
在P60,P70,P80和P90子集上分别获得了7.26%,2.57%,2.59%和4.20%的精度改进.
该框架有效地处理可变长度,并捕获非线性残留物相互作用.

结论:

AOP-DRL为识别抗氧化的实验方法提供了一个具有成本效益和可扩展的替代方案.
这种人工智能驱动的方法加速了治疗和营养的发现.
该框架的适应性表明,在个性化药物和功能性食品的生物活性预测中,其潜在应用范围更广.

关键词:

抗氧化剂类是一种抗氧化剂.深度表示学习学习 (deep representation learning) 是一种深度表示学习.酸序列预测的预测.蛋白质语言模型的模型文本-CNN 在线播放

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