# Deciphering Fingerprints of 3D Molecular Surfaces for Accurate Epitope Prediction

arXiv: https://arxiv.org/abs/2606.23830 · HTML: https://mlsanity.com/paper/2606.23830/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 85 references that are in the map.
- First posted on arXiv 22 Jun 2026.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [A Point Set Generation Network for 3D Object Reconstruction from a Single Image](https://mlsanity.com/paper/1612.00603/) (2016)
- [Protein Representation Learning by Geometric Structure Pretraining](https://mlsanity.com/paper/2203.06125/) (2022)
- [PointGPT: Auto-regressively Generative Pre-training from Point Clouds](https://mlsanity.com/paper/2305.11487/) (2023)
- [Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs](https://mlsanity.com/paper/2110.01191/) (2021)
- [GeoMAE: Masked Geometric Target Prediction for Self-supervised Point Cloud Pre-Training](https://mlsanity.com/paper/2305.08808/) (2023)
- [DeepSurf: A surface-based deep learning approach for the prediction of ligand binding sites on proteins](https://mlsanity.com/paper/2002.05643/) (2020)
- [Pre-training of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding](https://mlsanity.com/paper/2204.08663/) (2022)
- [Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement](https://mlsanity.com/paper/2207.06616/) (2022)
- [Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks](https://mlsanity.com/paper/2212.03447/) (2022)
- [Neural message passing for joint paratope-epitope prediction](https://mlsanity.com/paper/2106.00757/) (2021)
- [AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction](https://mlsanity.com/paper/2407.18184/) (2024)
- [PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking](https://mlsanity.com/paper/2505.01700/) (2025)
- [Proteo-R1: Reasoning Foundation Models for De Novo Protein Design](https://mlsanity.com/paper/2605.02937/) (2026)
- [Predicting mutational effects on protein binding from folding energy](https://mlsanity.com/paper/2507.05502/) (2025)
- [Surface-based Molecular Design with Multi-modal Flow Matching](https://mlsanity.com/paper/2601.04506/) (2026)
- [A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation](https://mlsanity.com/paper/2601.04507/) (2026)
- [Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling](https://mlsanity.com/paper/2601.06214/) (2026)

## Abstract (arXiv)

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.