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CKAN-ATHP:一种基于样本增量和损失改善策略的抗高血压的预测剂,使用卷积的科尔摩戈罗夫-阿诺德网络

Sen Yang^1,2, Jiaqi Ni², Xinye Ni³

¹Changzhou Second People's Hospital, The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center, Nanjing Medical University, Changzhou, 213003, China.

Journal of cheminformatics

|March 9, 2026

概括

一个新的框架,CKAN-ATHP,通过整合蛋白质语言模型和化学描述器,准确地预测抗高血压 (AHTP),在不平衡的数据条件下优于现有方法.

科学领域:

生物化学生化学
生物信息学是一种生物信息学.
计算生物学计算生物学

背景情况:

抗高血压 (AHTP) 通过抑制ACE和调节-血管新生素系统,有望降低血压.
由于其生物活性和安全性,AHTP在功能性食品和制药研究中越来越受到关注.

研究的目的:

开发CKAN-ATHP,一种用于识别抗高血压的新型预测框架.
提高AHTP的预测能力,特别是在具有挑战性的类不平衡数据条件下.

主要方法:

集成了三个预训练编码器 (DistilProtBert,PubChem10M,Prot-T5) 用于序列表示.
在特征空间使用样本增强策略来解决数据不平衡.
为了模型优化,使用了具有混合损失函数的卷积科尔莫戈罗夫-阿诺德网络 (CKAN).

主要成果:

在类不平衡的条件下,CKAN-ATHP的性能超过了七种最先进的方法.
在马修斯相关系数 (MCC) 中取得了12-24%的显著改善,在平衡精度 (BACC) 中获得了5-15%的显著改善.
通过案例研究和核密度估计分析,证明了强度和区分能力.

结论:

CKAN-ATHP为抗高血压预测提供了一种高效和强大的方法.
该框架将蛋白质语言模型嵌入与化学分子描述符集成在一起.
4-7的残留物被确定为抗高血压识别的关键.

关键词:

这是一种抗高血压.卷积的科尔摩戈罗夫阿诺尔德网络.深度学习是一种深度学习.标签光滑标签光滑标签光滑

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