mlsanityData to 14 Oct 2026

ABodyBuilder3: Improved and scalable antibody structure predictions

arXiv 2405.20863 ↗First posted 31 May 2024q-bio.BM cs.AIcited by 4

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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.

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

5 of 40 references.

References · 5

  1. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  2. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 843
  3. 2019On the Variance of the Adaptive Learning Rate and Beyondcited by 772
  4. 2024xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Proteincited by 50
  5. 2024Large scale paired antibody language modelscited by 16

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.