bioRxiv: Augmentation strategies boost protein language model performance on limited data

[bioRxiv] A preprint study examined how protein language models learn from suboptimal datasets and tested augmentation approaches to improve predictive performance. Stump et al. said that narrowing the distribution of protein-protein complexes in a limited dataset allows an order-of-magnitude decrease in training dataset size if a model is only expected to generalize to in-distribution data. In active learning, both in- and out-of-distribution Matthews Correlation Coefficient (MCC) showed a small but consistent increase after augmenting the training set with complexes produced by ESM-3, a generative protein language model, according to the authors. The work has not been peer-reviewed.