在图形神经网络中基于交互的诱导偏差:从3D结构中增强蛋白质-连接物结合亲和力预测
概括
我们为机器学习模型引入了基于交互的诱导偏差,以预测蛋白质-连接体结合亲和力. 这种方法通过建模原子相互作用来提高预测准确性和可解释性,优于现有的方法.
科学领域:
- 计算生物学 计算生物学
- 机器学习 机器学习
背景情况:
- 机器学习 (ML) 模型用于预测蛋白质-连接体结合亲和力 (PLA) 在诱导偏差方面有所不同,影响了概括性和解释性.
- 现有的ML方法往往与生物结合机制不一致,限制了预测能力和机制理解.
研究的目的:
- 为ML模型提出基于相互作用的诱导偏差,以改善PLA预测.
- 开发一种可解释的异质交互图神经网络 (EHIGN),体现这种偏差.
- 确保预测是基于生物学上相关的原子相互作用.
主要方法:
- 代表蛋白质-连接体复合体作为具有共价和非共价相互作用的异质图.
- 假设PLA是来自非共价相互作用的双向原子-原子亲和关系的总和.
- 实现EHIGN以模拟3D结构中的对原子相互作用.
主要成果:
- 与最先进的ML基线相比,EHIGN在PLA预测和虚拟选中表现出优越的概括能力.
- 分析证实了基于相互作用的诱导偏差指导物理现实的原子相互作用的学习.
- EHIGN准确地预测了Nirmatrelvir对SARS-CoV-2变种的疗效,提供了有意义的解释.
结论:
- 拟议的基于相互作用的诱导偏差增强了用于PLA预测的ML模型.
- 通过专注于物理上相关的原子相互作用,EHIGN提供了更好的概括性和可解释性.
- 这种方法在药物疗效预测和理解变异特异性相互作用方面具有实际实用性.
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