2025/01/30 by Manuel F. Mollon, Joaquín González-Rodríguez, Mollon, Manuel F. +7
Computer Science · Biochemistry, Genetics and Molecular Biology · #Natural Language Processing Techniques #Topic Modeling #Biomedical Text Mining and Ontologies
paper · pdf · doi:10.48550/arxiv.2501.18223
In this study, we expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks-by assessing the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset. Unlike larger, more diverse benchmarks such as ProteinGym, which cover a broad spectrum of tasks, FLIP focuses on constrained settings where data availability is limited. This makes it an ideal framework to evaluate model performance in scenarios with scarce task-specific data. We investigate whether recent advances in protein language models lead to significant improvements in such settings. Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.