PLAIG:使用一种新的基于相互作用的图形神经网络框架来预测蛋白质 - 连接物结合的亲和力
Madhav V Samudrala1, Somanath Dandibhotla2, Arjun Kaneriya3
1College of Arts and Sciences, The University of Virginia, Charlottesville, Virginia 22903, United States.
ACS bio & med chem Au
|June 25, 2025
概括
我们开发了PLAIG,一个图形神经网络 (GNN) 模型,用于准确地预测蛋白质-连接体结合亲和力. 通过整合分子拓和相互作用,PLAIG提高了概括性,超过了药物发现的现有方法.
科学领域:
- 计算化学是一种计算化学.
- 机器学习是机器学习.
- 药物发现 药物发现
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.
- 现有的机器学习模型由于不完整的特征表示而难以进行概括.
- 在开发强大的预测模型方面,过度装配仍然是一个挑战.
研究的目的:
- 开发一种通用机器学习框架,用于预测蛋白质 - 配体结合亲缘关系.
- 解决现有模型在概括和过拟合方面的局限性.
- 创建一种新的方法,整合结构和交互特征,以提高预测准确度.
主要方法:
- 开发了PLAIG,一个图形神经网络 (GNN) 框架,以图形形式表示绑定复合体.
- 集成的蛋白质-连接体相互作用和分子拓学,以捕捉独特的特征.
- 采用主要组件分析 (PCA) 和集体学习 (堆积回归器) 来减轻过度拟合.
主要成果:
- PLAIG在PDBbind v.2019精炼集上实现了0.78的皮尔森相关系数 (PCC),在v.2016核心集上达到0.82.
- 对DUDE-Z的外部验证显示平均AUC为0.69,区分了活性干与诱.
- 将PLAIG与其他方法集成的混合模型达到0.88的平均PCC,最高为0.98.
结论:
- PLAIG提供了一种强大的和通用的方法来预测蛋白质-联体结合亲和力.
- 基于GNN的框架有效地整合了各种分子特征,优于现有的模型.
- 未来的工作重点将集中在结合对接方法和评估de novo配体的性能上.
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