利用不同的距离函数来预测ESMFold预测的三级结构的几何深度学习的抗病毒
Greneter Cordoves-Delgado1, César R García-Jacas2,3, Yovani Marrero-Ponce4,5
1Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de Mexico, Km. 107 Carretera Tijuana-Ensenada, Ensenada 22860, Baja California, Mexico.
Antibiotics (Basel, Switzerland)
|January 28, 2026
概括
在结构图表中探索超出欧几里德距离的替代距离函数显著改善了抗病毒的预测. 这增强了用于药物发现的机器学习模型.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 机器学习加速了基于的药物发现.
- 图形学习框架使用结构图形.
- 目前的方法依赖于欧几里德距离值,没有强有力的证据.
研究的目的:
- 调查结构图表生成的各种距离函数.
- 使用这些图表来训练深度图形学习模型进行抗病毒预测.
主要方法:
- 使用各种距离函数分析氨基酸的接近程度.
- 根据不同的距离指标和随机图进行比较.
- 训练和评估具有最佳图形表示的深度图形学习模型.
主要成果:
- 不同的距离函数创建不同的图形编码不同的化学空间.
- 这些替代图表提高了预测模型的区分能力.
- 与最先进的模型进行了性能比较.
结论:
- 欧几里德距离对于全面的结构图表表示是不够的.
- 采用多种距离函数可以产生优异的图形结构.
- 优化图形表示导致增强的抗病毒预测模型.
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