We investigate the potential of patent data for improving the antibody humanness prediction using a multi-stage, multi-loss training process. Humanness serves as a proxy for the immunogenic response to antibody therapeutics, one of the major causes of attrition in drug discovery and a challenging obstacle for their use in clinical settings. We pose the initial learning stage as a weakly-supervised contrastive-learning problem, where each antibody sequence is associated with possibly multiple identifiers of function and the objective is to learn an encoder that groups them according to their patented properties. We then freeze a part of the contrastive encoder and continue training it on the patent data using the cross-entropy loss to predict the humanness score of a given antibody sequence. We illustrate the utility of the patent data and our approach by performing inference on three different immunogenicity datasets, unseen during training. Our empirical results demonstrate that the learned model consistently outperforms the alternative baselines and establishes new state-of-the-art on five out of six inference tasks, irrespective of the used metric.

利用专利数据，通过多阶段、多损失训练过程，研究了改进抗体人性预测的潜力，人性作为抗体治疗免疫原性反应的替代指标，从而解决了药物研发中的一大原因以及其在临床设置中的挑战障碍，并且通过对三个不同的免疫原性数据集进行推理，展示了专利数据和该方法的实用性，实验证明了该学习模型在六个推理任务中五个使用指标的情况下始终优于其他对照基准并创造了新的最优结果。

利用专利数据提升抗体人源性预测