arXiv Machine Learning
Learning Task-Specific Antibody Representations via Function-Aware Masking
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How to make the most of your masked language model for protein engineering
The paper introduces a flexible sampling technique for masked language models (MLMs) called stochastic beam search, which leverages MLMs’ efficiency in evaluating the pseudo‑perplexity of a sequence’s 1‑edit neighborhood. This method reframes generation as whole‑sequence evaluation, allowing guidance across multiple optimization objectives. Extensive in‑silico and in‑vitro tests on antibody therapeutics demonstrate that the sampling strategy significantly influences outcomes, highlighting the need for further research in this area.