# ABodyBuilder3: Improved and scalable antibody structure predictions

arXiv: https://arxiv.org/abs/2405.20863 · HTML: https://mlsanity.com/paper/2405.20863/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 40 references that are in the map.
- First posted on arXiv 31 May 2024.

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

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein](https://mlsanity.com/paper/2401.06199/) (2024)
- [Large scale paired antibody language models](https://mlsanity.com/paper/2403.17889/) (2024)

## Built on by (most cited first, 4 of 4)

- [Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins](https://mlsanity.com/paper/2507.09054/) (2025)
- [IgCraft: A versatile sequence generation framework for antibody discovery and engineering](https://mlsanity.com/paper/2503.19821/) (2025)
- [Conditionally Site-Independent Neural Evolution of Antibody Sequences](https://mlsanity.com/paper/2602.18982/) (2026)
- [Correcting the Dropout-LayerNorm Expectation Gap Improves Protein Structure Models](https://mlsanity.com/paper/2609.32062/) (2026)

## Abstract (arXiv)

Accurate prediction of antibody structure is a central task in the design and development of monoclonal antibodies, notably to understand both their developability and their binding properties. In this article, we introduce ABodyBuilder3, an improved and scalable antibody structure prediction model based on ImmuneBuilder. We achieve a new state-of-the-art accuracy in the modelling of CDR loops by leveraging language model embeddings, and show how predicted structures can be further improved through careful relaxation strategies. Finally, we incorporate a predicted Local Distance Difference Test into the model output to allow for a more accurate estimation of uncertainties.