UniKineG: Unified-Coordinate Geometric Graphs Enable Robust Enzyme Kinetic Prediction

Enzyme kinetic parameters (kcat, Km, and kcat/Km) are fundamental for quantifying catalytic efficiency and substrate specificity in biochemistry and drug discovery. However, experimental determination is resource intensive, and accurate prediction remains a persistent challenge due to the complex spatial nature of catalysis. In this paper, we present UniKineG, a novel deep learning framework that redefines kinetic prediction by modeling the explicit spatial state of enzyme-substrate complexes. Unlike conventional methods that treat proteins and ligands as isolated modalities, UniKineG employs molecular docking to embed both entities into a unified 3D coordinate system. Within this shared geometric context, we utilize a heterogeneous graph neural network integrated with geometric vector perceptrons (GVPs) to capture intricate vector-based interactions, such as directional hydrogen bonds, hydrophobic contacts, and electrostatic complementarity. This structure-based approach confers exceptional robustness: UniKineG effectively overcomes the dependency on high-sequence homology, demonstrating superior generalization on out-of-distribution (OOD) datasets encompassing both unseen enzyme sequences and diverse substrate scaffolds. Consistently outperforming state-of-the-art predictors, UniKineG achieves high-precision predictions. This work establishes a solid foundation for understanding enzyme-small molecule interactions in 3D space and offers a transformative tool for computational enzymology.