arXiv:2606.23830v1 Announce Type: cross 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 mo
The convergence of advanced AI, particularly Transformer architectures, with increasing computational power makes deeper analysis of complex biological structures like molecular surfaces feasible.
Accurate epitope prediction is critical for accelerating drug discovery, vaccine development, and therapeutic antibody design, directly impacting public health and pharmaceutical innovation.
Current limitations in identifying discontinuous, surface-driven epitopes are addressed by a surface-centric AI framework, moving beyond sequence or backbone structure-based methods.
- · Pharmaceutical R&D
- · Biotech companies
- · AI in healthcare
- · Immunotherapy developers
- · Traditional drug discovery methods
- · Companies reliant on less precise prediction models
Improved lead optimization and reduced failure rates in preclinical drug development.
Faster time to market for new vaccines and biologics, potentially lowering healthcare costs.
Enhanced pandemic preparedness and capability to rapidly design countermeasures against novel pathogens.
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Read at arXiv cs.AI