Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
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Computer Science > Machine Learning
Title:Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
Abstract:Molecular surfaces encode the geometric and physicochemical patterns that determine antibody-antigen recognition, central to epitope prediction. However, existing methods rely on sequences or backbone structures and struggle to capture discontinuous, surface-driven epitopes. This study presents SurfBind, a surface-centric learning framework for epitope prediction that operates directly on molecular surface representations. SurfBind integrates geometric and physicochemical cues through a Transformer-based architecture with patch-level surface modeling, binder-aware cross-attention, and a hierarchical coarse-to-fine prediction paradigm. Experiments on challenging epitope identification benchmarks, including SAbDab and DB5.5, demonstrate that SurfBind achieves state-of-the-art performance and strong generalization across unseen antibodies and conformational states, highlighting the value of interaction-aware surface modeling for understanding the crucial mechanisms of protein-protein interactions.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2606.23830 [cs.LG] |
| (or arXiv:2606.23830v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.23830
arXiv-issued DOI via DataCite (pending registration)
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| Journal reference: | KDD 2026 AI4Science |
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