通过物理感知图形网络探索多尺度交互特征,以增强绑定亲和度预测
Chang Cai1, Mugang Lin1,2,3, Wenjun Li4
1College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China.
Journal of chemical information and modeling
|December 23, 2025
概括
MSPANet是一种新的深度学习模型,通过将物理意识原理与科尔摩戈罗夫-阿诺德网络集成,准确地预测蛋白质-连接体结合亲和力. 这种方法通过提高预测准确性和概括性来增强分子识别和药物发现.
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:
- 蛋白质-连接体结合亲和力对于分子识别和药物发现至关重要.
- 当前的计算方法在准确建模复杂的3D交互方面面临挑战,导致预测准确性和概括性的局限性.
- 现有的方法往往过于简化了交互,在计算上昂贵,或者无法充分利用角特征.
研究的目的:
- 开发一个准确和可概括的计算框架来预测蛋白质 - 连接体结合亲和力.
- 通过结合物理意识原则和先进的神经网络架构来解决现有方法的局限性.
- 通过增强的蛋白质-连接体相互作用的建模,提高对分子识别的理解.
主要方法:
- 介绍MSPANet (多尺度物理意识网络),利用Kolmogorov-Arnold网络 (KAN) 来学习多尺度的空间相互作用.
- 用辐射和球形基础函数表示蛋白质-连接体复合体作为具有丰富距离和角度特征的原子图.
- 实施一个多尺度信息传播机制和一个注意力加权的融合模块,以捕捉动态和上下文交互.
主要成果:
- 在PDBbind v.2020基准测试中,MSPANet表现优异,在RMSE,MAE,标准偏差和Pearson's R.中实现了8-12%的相对改善.
- 该模型在CSAR-HiQ数据集上显示了持续的性能增长,突出了其稳定性和通用性.
- KAN层增强了模型的表达性和可解释性,而物理意识的几何编码提高了预测准确性.
结论:
- MSPANet提供了一个可扩展和可解释的框架,用于蛋白质 - 连接体结合亲缘关系的预测.
- 将物理意识的几何编码与KAN驱动的表示学习集成为生物分子建模提供了一个强大的工具.
- 这种方法推进了分子识别和药物发现领域,通过使更准确和可靠的亲和力预测成为可能.
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