  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.
