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.

基于 ImmuneBuilder，我们介绍了 ABodyBuilder3，一种改进和可扩展的抗体结构预测模型，通过利用语言模型嵌入，实现了 CDR 环的建模中的最新准确性，并展示了通过仔细的松弛策略进一步改进预测结构的方法。最后，我们将预测的局部距离差异测试结合到模型输出中，以便更准确地估计不确定性。

ABodyBuilder3: 改良和可扩展的抗体结构预测

ABodyBuilder3: Improved and scalable antibody structure predictions

Monoclonal antibodies (mAbs) have emerged as indispensable assets in
medicine, and are currently at the forefront of biopharmaceutical product
development. However, the growing market demand and the substantial doses
required for mAb clinical treatments necessitate significant progress in its
large-scale production. Most of the processes for industrial mAb production
rely on batch operations, which result in significant downtime. The shift
towards a fully continuous and integrated manufacturing process holds the
potential to boost product yield and quality, while eliminating the extra
expenses associated with storing intermediate products. The integrated
continuous mAb production process can be divided into the upstream and
downstream processes. One crucial aspect that ensures the continuity of the
integrated process is the switching of the capture columns, which are typically
chromatography columns operated in a fed-batch manner downstream. Due to the
discrete nature of the switching operation, advanced process control algorithms
such as economic MPC (EMPC) are computationally difficult to implement. This is
because an integer nonlinear program (INLP) needs to be solved online at each
sampling time. This paper introduces two computationally-efficient approaches
for EMPC implementation, namely, a sigmoid function approximation approach and
a rectified linear unit (ReLU) approximation approach. It also explores the
application of deep reinforcement learning (DRL). These three methods are
compared to the traditional switching approach which is based on a 1% product
breakthrough rule and which involves no optimization.

单克隆抗体的持续生产是一个重要的研究领域。本文介绍了两种计算高效的经济模型预测控制（EMPC）实现方法，即 S 型函数逼近方法和修正线性单元（ReLU）逼近方法，同时探讨了深度强化学习方法的应用。这三种方法与传统的切换方法进行了比较，传统方法基于 1％产品突破规则，不涉及优化。