Over the past 40 years, the discovery and development of therapeutic
antibodies to treat disease has become common practice. However, as therapeutic
antibody constructs are becoming more sophisticated (e.g., multi-specifics),
conventional approaches to optimisation are increasingly inefficient. Machine
learning (ML) promises to open up an in silico route to antibody discovery and
help accelerate the development of drug products using a reduced number of
experiments and hence cost. Over the past few years, we have observed rapid
developments in the field of ML-guided antibody discovery and development
(D&D). However, many of the results are difficult to compare or hard to assess
for utility by other experts in the field due to the high diversity in the
datasets and evaluation techniques and metrics that are across industry and
academia. This limitation of the literature curtails the broad adoption of ML
across the industry and slows down overall progress in the field, highlighting
the need to develop standards and guidelines that may help improve the
reproducibility of ML models across different research groups. To address these
challenges, we set out in this perspective to critically review current
practices, explain common pitfalls, and clearly define a set of method
development and evaluation guidelines that can be applied to different types of
ML-based techniques for therapeutic antibody D&D. Specifically, we address in
an end-to-end analysis, challenges associated with all aspects of the ML
process and recommend a set of best practices for each stage.

过去 40 年来，治疗抗体的发现和开发已经变得司空见惯。然而，随着治疗抗体构造变得更复杂（例如多样性抗体），常规的优化方法日益低效。机器学习承诺开辟一条使用较少实验和成本的计算模拟路径来加速药物产品的发现和开发。本文对当前的做法进行了批判性评论，解释了常见陷阱，并明确了一组方法开发和评估指南，可应用于治疗抗体发现与开发的不同类型的机器学习技术。