Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark

• URL: http://arxiv.org/abs/2501.18223v1
• Date: Thu, 30 Jan 2025 09:24:58 GMT
• Title: Exploring Large Protein Language Models in Constrained Evaluation Scenarios within the FLIP Benchmark
• Authors: Manuel F. Mollon, Joaquin Gonzalez-Rodriguez, Alicia Lozano-Diez, Daniel Ramos, Doroteo T. Toledano,
• Abstract summary: We expand upon the FLIP benchmark-designed for evaluating protein fitness prediction models in small, specialized prediction tasks.<n>We assess the performance of state-of-the-art large protein language models, including ESM-2 and SaProt on the FLIP dataset.<n>Our findings provide valuable insights into the performance of large-scale models in specialized protein prediction tasks.
• Score: 1.7446273568461808
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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