基于机器学习和深度学习的最新趋势,对G蛋白合受体-连接体结合 afinities 的预测
Joshua Stephenson1, Konda Reddy Karnati1
1Department of Natural Sciences, Bowie State University, Bowie, MD, United States.
Frontiers in bioinformatics
|January 28, 2026
概括
预测蛋白质 - 配体结合亲和力对于药物发现至关重要. 本综述将机器学习模型 (基于序列,图形和结构) 分类为预测结合亲和关系,特别是对于G蛋白结合受体 (GPCRs).
科学领域:
- 计算化学和化学信息学
- 药理学和药物发现
- 生物信息学和机器学习
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于有效的药物发现至关重要.
- G蛋白结合受体 (GPCR) 是一种关键的药物标类,但它们的结构多样性带来了挑战.
- 机器学习 (ML) 和深度学习 (DL) 在推进绑定亲和力预测方面表现有前途.
研究的目的:
- 审查和分类现有的ML/DL模型来预测蛋白质 - 配体结合的亲缘关系.
- 检查基于序列 (1D),基于图形 (2D) 和基于结构 (3D) 框架的模型.
- 突出混合方法的潜力,以提高药物发现成功率,特别是GPCRs.
主要方法:
- 将绑定亲和力预测模型分为基于序列,基于图形和基于结构的框架.
- 在每个框架内讨论特定的ML/DL技术,包括卷积神经网络 (CNN),图形神经网络 (GNN),注意力机制和自我监督学习.
- 基于结构的模型的空间和结构数据的整合.
主要成果:
- 基于序列的模型,包括CNN,对于高通量选是有效的.
- 先进的模型包括注意力机制和自我监督学习,以提高可解释性和减少数据需求.
- 基于图形的模型利用GNN和分子接触图进行拓特征捕获和对基底结构敏感的预测.
- 基于结构的方法利用空间和形状数据进行高分辨率交互建模.
结论:
- 结合序列,图形和基于结构的ML/DL模型的混合方法为提高*in silico*药物发现的准确性提供了显著的潜力.
- 更好地预测结合亲缘关系,特别是对于像GPCRs这样具有挑战性的标,可以加快新疗法的识别.
- 这些多样化的建模策略的进一步开发和整合对于推进计算药物发现至关重要.
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