# ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning

arXiv: https://arxiv.org/abs/2605.21600 · HTML: https://mlsanity.com/paper/2605.21600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 20 May 2026.

## Builds on (most cited first, 15 of 15)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [E(n) Equivariant Graph Neural Networks](https://mlsanity.com/paper/2102.09844/) (2021)
- [On the Bottleneck of Graph Neural Networks and its Practical Implications](https://mlsanity.com/paper/2006.05205/) (2020)
- [Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design](https://mlsanity.com/paper/2110.04624/) (2021)
- [Conditional Antibody Design as 3D Equivariant Graph Translation](https://mlsanity.com/paper/2208.06073/) (2022)
- [End-to-End Full-Atom Antibody Design](https://mlsanity.com/paper/2302.00203/) (2023)
- [AbODE: Ab Initio Antibody Design using Conjoined ODEs](https://mlsanity.com/paper/2306.01005/) (2023)
- [VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification](https://mlsanity.com/paper/2404.07194/) (2024)
- [dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen](https://mlsanity.com/paper/2503.01910/) (2025)
- [Retrieval Augmented Diffusion Model for Structure-informed Antibody Design and Optimization](https://mlsanity.com/paper/2410.15040/) (2024)
- [Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding](https://mlsanity.com/paper/2506.20957/) (2025)
- [AsEP: Benchmarking Deep Learning Methods for Antibody-specific Epitope Prediction](https://mlsanity.com/paper/2407.18184/) (2024)
- [Relation-Aware Equivariant Graph Networks for Epitope-Unknown Antibody Design and Specificity Optimization](https://mlsanity.com/paper/2501.00013/) (2024)
- [AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion](https://mlsanity.com/paper/2505.12358/) (2025)
- [CHIMERA-Bench: A Benchmark Dataset for Epitope-Specific Antibody Design](https://mlsanity.com/paper/2603.13431/) (2026)

## Abstract (arXiv)

Computational antibody CDR design methods condition on antigen structure to generate binding loops. Yet, the existing architectures conflate two fundamentally distinct sub-problems: identifying which CDR positions will contact the antigen, and selecting amino acids at those positions. This forces models to learn contact reasoning implicitly through uniform message passing, diluting antigen signal across all positions equally. We introduce ConTact, a contact-then-act architecture that explicitly decomposes CDR design into three cascaded stages: learning surface complementarity fingerprints, predicting CDR-antigen contacts, and injecting contact-gated antigen features into the prediction head. A distance-biased cross-attention module encodes geometric priors favoring spatial neighbors, a contact-weighted cross-entropy loss concentrates gradient signal on binding-critical positions, and a standoff-aware contact-recovery loss places the designed loop against its native epitope. On the ChiMERa-Bench dataset, ConTact surpasses the baselines on the binding interface and structural metrics (backbone RMSD, fraction of native contacts, interface RMSD, DockQ, and epitope F1) on all three splits, improving native-contact recovery by 11 to 16%, while matching the best sequence recovery. The source code is available at: https://github.com/mansoor181/ConTact.git