# Precise Antigen-Antibody Structure Predictions Enhance Antibody Development with HelixFold-Multimer

arXiv: https://arxiv.org/abs/2412.09826 · HTML: https://mlsanity.com/paper/2412.09826/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 59 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [HelixDesign-Antibody: A Scalable Production-Grade Platform for Antibody Design Built on HelixFold3](https://mlsanity.com/paper/2507.02345/) (2025)

## Abstract (arXiv)

The accurate prediction of antigen-antibody structures is essential for advancing immunology and therapeutic development, as it helps elucidate molecular interactions that underlie immune responses. Despite recent progress with deep learning models like AlphaFold and RoseTTAFold, accurately modeling antigen-antibody complexes remains a challenge due to their unique evolutionary characteristics. HelixFold-Multimer, a specialized model developed for this purpose, builds on the framework of AlphaFold-Multimer and demonstrates improved precision for antigen-antibody structures. HelixFold-Multimer not only surpasses other models in accuracy but also provides essential insights into antibody development, enabling more precise identification of binding sites, improved interaction prediction, and enhanced design of therapeutic antibodies. These advances underscore HelixFold-Multimer's potential in supporting antibody research and therapeutic innovation.