Transfer Message Passing for Molecular Property and Function Prediction across Polymer and Protein Graphs

Abstract

The prediction of molecular properties and biological functions across vastly different scales of macromolecules presents a formidable challenge in computational chemistry and bioinformatics. Traditional computational approaches often treat synthetic polymers and biological proteins as fundamentally distinct entities, applying specialized algorithms that fail to leverage the shared topological and chemical principles underlying both domains. This paper introduces a novel computational framework based on Transfer Message Passing, designed to unify the representation and predictive modeling of polymer and protein graphs. By conceptualizing both classes of macromolecules as complex, attributed graphs where nodes represent constituent functional units and edges denote chemical or spatial interactions, we establish a generalized topological space suitable for advanced graph neural networks. The proposed transfer learning mechanism dynamically adapts message passing operations learned from data-rich protein databases to infer complex physical and thermodynamic properties in specialized polymer datasets, mitigating the pervasive issue of data scarcity in polymer informatics. Extensive empirical evaluations demonstrate that our framework significantly outperforms domain-specific baseline models in predicting polymer bandgaps, glass transition temperatures, and protein enzymatic functions. Furthermore, detailed ablation studies reveal that the cross-domain attention mechanisms effectively align latent representations without compromising task-specific predictive accuracy. Ultimately, this research provides a robust theoretical foundation and a scalable computational tool for accelerated materials discovery and biomolecular engineering.