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.

翻译：抗体结构的准确预测是单克隆抗体设计与开发中的核心任务，尤其对于理解其可开发性与结合特性至关重要。本文介绍了ABodyBuilder3——一个基于ImmuneBuilder改进且可扩展的抗体结构预测模型。通过利用语言模型嵌入，我们在CDR环建模中实现了新的最先进精度，并展示了如何通过精细的弛豫策略进一步优化预测结构。最后，我们在模型输出中引入了预测的局部距离差异检验，以实现更准确的不确定性估计。