CL-GNN:对比学习和图形神经网络用于蛋白质-连接物结合亲和力预测
Yunjiang Zhang1, Chenyu Huang1, Yaxin Wang1
1Department of Chemical Engineering and Technology, College of Materials Science and Engineering, Beijing University of Technology, Beijing 100124, P. R. China.
Journal of chemical information and modeling
|February 6, 2025
概括
这项研究引入了一种新的自主监督学习框架,使用对比学习和图形神经网络来预测蛋白质-连接体结合亲和力. 该方法通过有效地从未标记的数据中学习并提供可解释的见解来加速药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
- 传统的方法往往是昂贵和耗时的.
- 需要有效的计算方法.
研究的目的:
- 引入一种新的自主监督学习 (SSL) 框架,将对比学习和图形神经网络 (CL-GNN) 结合起来,用于预测蛋白质-连接体结合 afinities.
- 开发一种更有效的药物发现计算方法.
- 提高结合亲和力预测模型的可解释性.
主要方法:
- 利用对比式学习策略,一种SSL形式,对371,458个未标记的蛋白质连接体复合体的大数据集进行了研究.
- 采用图形神经网络和分子图形增强技术,以进行蛋白质 - 配体相互作用的自我监督学习.
- 通过共弦相似度评估学习的表示之间的相似性.
主要成果:
- 微调的CL-GNN模型在基准数据集上实现了高皮尔森相关系数和低平方根平均误差的竞争性表现.
- 拟议的方法在预测绑定亲和关系方面优于现有的机器学习模型.
- 该方法揭示了复杂物之间的潜在联系,并提供了对药物作用机制的洞察.
结论:
- CL-GNN框架提供了一种强大而高效的方法来预测蛋白质-连接体结合亲缘关系,加速药物开发.
- 该模型的可解释性,通过对关键残留物和原子的可视化来增强,为药物设计提供了宝贵的生物学见解.
- 这种自我监督的学习方法证明了推进计算药物发现的巨大潜力.
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