Protein Language Models Under Constraints
Evaluating large protein models in limited-data scenarios
This study evaluates how large protein language models perform in specialized prediction tasks with limited data availability.
- Applies ESM-2 and SaProt models to the FLIP benchmark
- Focuses on constrained settings where data is scarce
- Provides a complementary evaluation to broader benchmarks like ProteinGym
- Offers insights into model performance in real-world biology scenarios
This research matters because protein fitness prediction is crucial for drug discovery and understanding protein function, particularly in scenarios where large datasets aren't available.