VQH-AM: Vector Quantization-Augmented Heterogeneous Graph Learning for Antibody Affinity Maturation Prediction

Modeling the impact of amino acid mutations on the binding affinity between antibodies and antigens plays a pivotal role in antibody therapeutics development. While numerous deep learning-based methods have shown promising results, accurately modeling the impact of mutations on binding interfaces remains a significant challenge, as even small changes can substantially affect binding affinity. To address this issue, we propose VQH-AM, a Vector Quantization-based Heterogeneous graph neural network for antibody Affinity Maturation prediction. Specifically, we construct atom-level heterogeneous graphs for both wild-type and mutant antibody-antigen complexes to model their binding interfaces. Atomic representations extracted by the heterogeneous graph neural networks are then quantized via a context-aware codebook designed to capture semantic atomic patterns. Finally, both the original and quantized atomic representations are integrated and used to predict the affinity maturation outcomes. Benchmark experiments demonstrate that VQH-AM consistently outperforms state-of-the-art methods across multiple evaluation metrics. The source code is available at: https://github.com/zwzhen-hnu/VQH-AM.