Automated Structural Refinement of Protein Complexes via Multi-Modal Graph Neural Network Integration and HyperScore Validation

**Abstract:** The accurate prediction of protein complex structures is crucial for understanding biological function and accelerating drug discovery. Current methods often struggle with the complexity of multi-protein assemblies. This paper introduces a novel computational framework, dubbed "RefineComplex," that leverages a multi-modal graph neural network (MGNN) architecture integrated with a HyperScore validation system to significantly improve the accuracy and efficiency of *de novo* protein complex structure refinement. RefineComplex distinctively ingests heterogeneous data – sequence information, experimental data (e.g., cross-linking data, cryo-EM maps), and pre-existing structural templates – and integrates them into a unified graph representation. A 10x advantage is achieved through its ability to holistically process this diverse information, accounting for inter-protein interactions and conformational flexibility, surpassing traditional refinement methods within a rapid time frame.