Robert Schmirler

This paper argues for evaluating protein language models through prospective experimental success rather than retrospective predictive accuracy.

DeePEn evaluates protein engineering models' performance on variants with increasing mutational distance from wild type using DMS datasets.

This study shows that fine-tuning protein language models improves prediction performance across tasks and can be done efficiently, especially for small datasets.