| --- |
| language: |
| - en |
| license: cc-by-4.0 |
| pretty_name: VibeProteinBench |
| configs: |
| - config_name: recognition |
| data_files: |
| - split: test |
| path: recognition/test-00000-of-00001.parquet |
| - config_name: engineering |
| data_files: |
| - split: test |
| path: engineering/test-00000-of-00001.parquet |
| - config_name: generation |
| data_files: |
| - split: test |
| path: generation/test-00000-of-00001.parquet |
| --- |
| |
| # VibeProteinBench |
|
|
| VibeProteinBench is a benchmark for evaluating language-interfaced protein design models across three tasks: protein recognition, rationale-guided engineering, and functional protein generation. |
|
|
| ## Configurations |
|
|
| - **recognition** — protein recognition tasks. |
| - **engineering** — human-eval-verified, rationale-guided protein engineering tasks. |
| - **generation** — functional protein generation tasks. |
|
|
| Each configuration is provided as a single `test` split in Parquet format. |
|
|
| ## Responsible AI (RAI) Metadata |
|
|
| ### Data limitations / known limitations |
| VibeProteinBench relies primarily on in silico validation metrics such as predicted structure confidence, energy changes, docking scores, GO prediction, and interface-quality measures, rather than experimental wet-lab validation. Therefore, benchmark success may not fully guarantee real-world biological function or experimental viability. |
|
|
| ### Bias and underrepresented populations |
| The benchmark is biased toward proteins, structures, annotations, and molecular targets available in public biological databases such as UniProt/Swiss-Prot, RCSB PDB, Gene Ontology, InterPro, ECOD, BRENDA, OGTFinder, PubChem, and related resources. Under-characterized protein families, organisms, or structures, may be underrepresented. |
|
|
| ### Personal or sensitive information |
| The dataset does not contain personal, human-identifiable, demographic, or private sensitive information. It consists of protein sequences, molecular targets, functional annotations, structural annotations, and benchmark queries derived from public biological databases. |
|
|
| ### Validated use cases and construct validity |
| The dataset is intended for evaluating language-interfaced protein design models across protein recognition, rationale-guided engineering, and functional protein generation. Construct validity is supported through expert-curated task design, mechanistic rationales, public biological annotations, and multi-faceted in silico validation, but not through experimental validation. |
|
|
| ### Potential societal impacts |
| This benchmark may support progress in computational biology, protein engineering, biotechnology, and therapeutic design by enabling more systematic evaluation of language-interfaced protein design models. However, improved protein design capability may also lower barriers to misuse, including harmful or toxic biological design, so responsible deployment and safeguards are important. |
|
|
| ### Synthetic data inclusion and source |
| The benchmark does not primarily consist of synthetic biological measurements or experimentally generated synthetic proteins. It includes constructed natural-language benchmark queries and evaluation tasks derived from natural protein sequences and experimentally-determined structures, expert-defined rationales, and computational preprocessing. |
|
|
| ### Source dataset provenance |
| The benchmark is derived from public biological and chemical resources including UniProt/Swiss-Prot, RCSB PDB, Gene Ontology, InterPro, ECOD, BRENDA, OGTFinder, PubChem, PubMed, and Semantic Scholar. Additional validation and annotation signals are obtained using computational tools such as Biopython, DSSP, Protenix-v1, PyRosetta, AutoDock Vina, pyKVFinder, GO-GPT, RDKit, and related pipelines. |
|
|
| ### Preprocessing / collection / annotation procedures |
| The dataset was curated by selecting proteins, GO terms, protein targets, and small-molecule targets from public databases with contamination controls such as temporal cutoffs and literature-based filtering. Queries were constructed for recognition, engineering, and generation stages using computed biochemical properties, structural annotations, expert-curated mechanistic rationales, and task-specific in silico evaluation criteria; human experts further reviewed tasks for realism, clarity, and verifiability. |
|
|
| ## License |
| Released under the Creative Commons Attribution 4.0 International License (CC-BY-4.0). |
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|
