Efficient and large-scale evaluation of antibody-antigen neutralization is critical for accelerating antibody drug development. To address this need, we propose SPAAN, a deep-learning framework that predicts neutralization directly from antibody and antigen sequences. Rather than relying on experimentally determined structures, SPAAN learns from structural knowledge and biologically relevant molecular properties during training, enabling accurate predictions using sequence information alone. On the SARS-CoV-2 neutralization dataset, SPAAN consistently outperforms existing state-of-the-art methods. The model also shows strong interpretability by capturing key interaction patterns underlying antibody-antigen recognition. Furthermore, on the HIV neutralization dataset, SPAAN achieves state-of-the-art performance in multiple challenging scenarios involving previously unseen antibodies or antigens, demonstrating robust generalization ability. Overall, SPAAN provides an accurate, interpretable, and broadly applicable framework for antibody-antigen neutralization prediction, offering a practical tool to support large-scale antibody engineering and therapeutic discovery.
Liu et al. (Sun,) studied this question.