VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917504770637824 |
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| author | Seo, Hyunjin Ahn, Hongjoon Park, Jimin Han, Sungjun Lee, Gyubok Yang, Soojung Brown, Joseph S Chen, Leo Nesr, Gina El Eweje, Feyisayo Gurev, Sarah Lee, Hyejin Liu, Cheng-Hao Liu, Junlang Qi, Zhihui Lee, Gyu Rie Ahn, Sungsoo Shin, Jamin Jung, Sangwon |
| author_facet | Seo, Hyunjin Ahn, Hongjoon Park, Jimin Han, Sungjun Lee, Gyubok Yang, Soojung Brown, Joseph S Chen, Leo Nesr, Gina El Eweje, Feyisayo Gurev, Sarah Lee, Hyejin Liu, Cheng-Hao Liu, Junlang Qi, Zhihui Lee, Gyu Rie Ahn, Sungsoo Shin, Jamin Jung, Sangwon |
| contents | Protein design aims to compose amino-acid sequences that fold into stable three-dimensional structures while satisfying targeted functional properties. The field is increasingly shifting toward vibe protein design, where a single model is expected to generate novel sequences, engineer existing proteins, and reason about protein characteristics through flexible natural-language constraints. Large language models (LLMs) have emerged as a leading paradigm in this space. However, existing evaluation benchmarks often limit their scope to a partial aspect of protein design, while others restrict design objectives to structured input schemas, lacking an integrated framework that evaluates the broad spectrum of protein design competence under open-ended intents. To this end, we present Vibe Protein design Benchmark (VibeProteinBench), a language-interfaced benchmark that probes generalist capabilities through three complementary stages mirroring a computational protein design workflow: recognition, engineering, and generation. Each stage is grounded in expert-curated mechanistic rationales and multi-faceted in silico validation, to computationally verify whether model outputs are biologically plausible. Evaluations across diverse general-purpose and domain-specialized LLMs reveal that no model achieves strong performance across all three stages, suggesting that generalist protein design remains a substantial open challenge for current LLMs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_10978 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design Seo, Hyunjin Ahn, Hongjoon Park, Jimin Han, Sungjun Lee, Gyubok Yang, Soojung Brown, Joseph S Chen, Leo Nesr, Gina El Eweje, Feyisayo Gurev, Sarah Lee, Hyejin Liu, Cheng-Hao Liu, Junlang Qi, Zhihui Lee, Gyu Rie Ahn, Sungsoo Shin, Jamin Jung, Sangwon Quantitative Methods Protein design aims to compose amino-acid sequences that fold into stable three-dimensional structures while satisfying targeted functional properties. The field is increasingly shifting toward vibe protein design, where a single model is expected to generate novel sequences, engineer existing proteins, and reason about protein characteristics through flexible natural-language constraints. Large language models (LLMs) have emerged as a leading paradigm in this space. However, existing evaluation benchmarks often limit their scope to a partial aspect of protein design, while others restrict design objectives to structured input schemas, lacking an integrated framework that evaluates the broad spectrum of protein design competence under open-ended intents. To this end, we present Vibe Protein design Benchmark (VibeProteinBench), a language-interfaced benchmark that probes generalist capabilities through three complementary stages mirroring a computational protein design workflow: recognition, engineering, and generation. Each stage is grounded in expert-curated mechanistic rationales and multi-faceted in silico validation, to computationally verify whether model outputs are biologically plausible. Evaluations across diverse general-purpose and domain-specialized LLMs reveal that no model achieves strong performance across all three stages, suggesting that generalist protein design remains a substantial open challenge for current LLMs. |
| title | VibeProteinBench: An Evaluation Benchmark for Language-interfaced Vibe Protein Design |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2605.10978 |