# Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins

arXiv: https://arxiv.org/abs/2507.09054 · HTML: https://mlsanity.com/paper/2507.09054/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 68 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [AlphaFold Meets Flow Matching for Generating Protein Ensembles](https://mlsanity.com/paper/2402.04845/) (2024)
- [Structure-based drug design with geometric deep learning](https://mlsanity.com/paper/2210.11250/) (2022)
- [ABodyBuilder3: Improved and scalable antibody structure predictions](https://mlsanity.com/paper/2405.20863/) (2024)
- [Generalizing to any diverse distribution: uniformity, gentle finetuning and rebalancing](https://mlsanity.com/paper/2410.05980/) (2024)
- [S2ALM: Sequence-Structure Pre-trained Large Language Model for Comprehensive Antibody Representation Learning](https://mlsanity.com/paper/2411.15215/) (2024)

## Built on by (most cited first, 2 of 2)

- [Tokenizing Loops of Antibodies](https://mlsanity.com/paper/2509.08707/) (2025)
- [Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models](https://mlsanity.com/paper/2609.32062/) (2026)

## Abstract (arXiv)

We introduce Ibex, a pan-immunoglobulin structure prediction model that achieves state-of-the-art accuracy in modeling the variable domains of antibodies, nanobodies, and T-cell receptors. Unlike previous approaches, Ibex explicitly distinguishes between bound and unbound protein conformations by training on labeled apo and holo structural pairs, enabling accurate prediction of both states at inference time. Using a comprehensive private dataset of high-resolution antibody structures, we demonstrate superior out-of-distribution performance compared to existing specialized and general protein structure prediction tools. Ibex combines the accuracy of cutting-edge models with significantly reduced computational requirements, providing a robust foundation for accelerating large molecule design and therapeutic development.