Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

• URL: http://arxiv.org/abs/2606.23830v1
• Date: Mon, 22 Jun 2026 18:14:14 GMT
• Title: Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction
• Abstract summary: SurfBind is a surface-centric learning framework for prediction that operates directly on molecular surface representations.<n>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.
• Score: 82.84146585955015
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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