.png){kind=icon}
返回
Exploring Multi-Scale Interaction Features through a Physics-Aware Graph Network for Enhanced Binding Affinity Prediction
.png){kind=icon}
PRE
AI
DOI：10.1021/acs.jcim.5c02480.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Protein-ligand binding affinity plays a central role in molecular recognition and drug discovery, yet accurate prediction remains challenging due to the complexity of three-dimensional interactions. Conventional computational approaches, including docking, physics-based free energy calculations, and graph neural networks (GNNs), have advanced this field but still suffer from oversimplified interaction modeling, high computational cost, insufficient angular feature utilization, and limited generalization. In this work, we present MSPANet (Multi-Scale Physics-Aware Network), which employs the Kolmogorov-Arnold Network (KAN) to learn multiscale spatial interactions under physics-aware guidance, thereby improving both accuracy and generalization in protein-ligand binding affinity prediction. MSPANet represents protein-ligand complexes as atomic graphs enriched with distance and angular features through radial and spherical basis functions, enabling the explicit encoding of both local interaction details and long-range dependencies. A multiscale information propagation mechanism is introduced to capture dynamic interactions across different levels without requiring dihedral angle calculations. Leveraging adaptive spline-based nonlinear transformations, KAN layers enhance model expressiveness and interpretability, while an attention-weighted fusion module integrates local and global structural contexts for robust prediction. Extensive evaluations on the PDBbind v.2020 benchmark demonstrate that MSPANet achieves superior performance, showing relative improvements of 8-12% over state-of-the-art methods across key metrics including RMSE, MAE, standard deviation, and Pearson's R. Consistent gains on the CSAR-HiQ data set further highlight its robustness and generalizability. Overall, this work establishes a scalable and interpretable framework that combines physics-aware geometric encoding with KAN-driven representation learning, offering a powerful tool for protein-ligand affinity prediction and related biomolecular modeling tasks.
Keyword:
PROTEIN
MACHINE
期刊
IF:
5.3
论文数:
9.2K
被引数:
4.0W
机构
引用论文
Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Determining Protein-Protein Interaction Using Support Vector Machine: A Review使用支持向量机确定蛋白质-蛋白质相互作用: 综述IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏分享收藏分享收藏
查看更多内容