ML-PLA:增强与微环境和长距离交互感知图形神经网络的蛋白质-连接物结合 afinity 预测
Yajie Meng1, Zhuang Zhang1, Jincan Li1
1School of Computer Science and Artificial Intelligence, Wuhan Textile University, Wuhan, Hubei 430200, China.
Journal of chemical information and modeling
|October 3, 2025
概括
ML-PLA通过整合序列和结构数据,准确地预测蛋白质 - 配体结合亲和力 (PLA). 这种新的方法捕捉复杂的微环境和远程相互作用,以改善药物发现的领先识别.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 准确地预测蛋白质 - 配体结合亲和力 (PLA) 对药物发现至关重要.
- 现有的基于图形的模型通常使用浅特征融合和固定距离值,限制它们捕捉复杂生物相互作用的能力.
研究的目的:
- 开发一种新的方法,ML-PLA,用于更好地预测蛋白质 - 配体结合亲和力.
- 解决目前关于微环境表示和远程相互作用建模的模型中的局限性.
主要方法:
- 利用异质图神经网络来聚合蛋白质微环境建模的序列和结构信息.
- 采用矢量量化变量自编码器用于多样化和化学意义上的微环境表示.
- 实施了多头注意力机制,将原子投射到虚拟原子中,捕捉长距离的相互作用而不会过度平滑.
主要成果:
- ML-PLA在预测蛋白质 - 配体结合亲和力方面表现出显著的有效性.
- 该方法在基准数据集 (CASF-2016和CASF-2013) 上显示出强大的概括能力.
- 在结合亲和力预测中超越了最先进的方法.
结论:
- ML-PLA提供了一种优越的方法来建模蛋白质微环境和远程相互作用,以准确地预测PLA.
- 该方法有望在药物发现中推进化合物识别.
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