Xiao, Y.; Zheng, Y.; Hua, Y.; Peng, J.; Liu, J.; Qu, Y.; Xu, J.; Fu, R.; Qian, Q.; Zhao, M.; Zhang, X.; Zhao, J.; Yao, Y.; Kosar, M.; Ke, Y.; Chi, Y.

High-throughput accurate protein-protein interaction (PPI) prediction is foundational to systems-level biological understanding, disease mechanism dissection, and structure-based drug discovery. Traditional graph convolutional networks (GCNs) are limited by discrete information propagation, layer-wise representation homogenization, and absent continuous-time state evolution, failing to capture residues' 3D spatial hierarchical dynamic binding patterns. We present LNGCN, a hybrid framework integrating liquid neural networks with GCNs, which encodes residue radial distances as node-level driving terms for continuous updates with hierarchical probabilistic calibration. On standard benchmarks, LNGCN achieves $90\%$ relative AUPRC improvement over PIPR, outperforms RF2-PPI on $1:10$ imbalanced datasets, and retains $0.9324$ AUPRC on held-out yeast test data. LNGCN further demonstrates biological utility in phosphorylation-dependent SHP2 signaling, FGF23-FGFR1c-$\alpha$-Klotho ternary assembly, Tdk1 oligomeric-state-dependent interactions, and experimentally validated TPR-mediated candidates. By capturing state-dependent interaction changes, LNGCN provides a scalable framework for PPI screening, candidate prioritization, and future residue-level dynamic PPI trajectory modeling.