| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - fill-mask |
| tags: |
| - biology |
| - protein-design |
| - ligand-binding |
| - antibody-design |
| configs: |
| - config_name: stage1 |
| data_files: |
| - split: train |
| path: stage1/train.jsonl |
| - split: validation |
| path: stage1/valid.jsonl |
| - split: test |
| path: stage1/test.jsonl |
| - config_name: ligand |
| data_files: |
| - split: train |
| path: ligand/train.jsonl |
| - split: validation |
| path: ligand/valid.jsonl |
| - split: test |
| path: ligand/test_ligand.jsonl |
| - split: test_unseen |
| path: ligand/test_ligand_unseen.jsonl |
| - config_name: antibody |
| data_files: |
| - split: train |
| path: antibody/train.jsonl |
| - split: validation |
| path: antibody/valid.jsonl |
| - split: test |
| path: antibody/final_antibody_test.jsonl |
| --- |
| |
| # InstructPro2Bench |
|
|
| Training and evaluation data for |
| [InstructPro2](https://huggingface.co/charlesnovak/InstructPro2), which |
| designs proteins from natural-language function descriptions. |
|
|
| [Paper](https://arxiv.org/abs/2610.19592) · |
| [Code](https://github.com/chovak/InstructPro2) · |
| [Model](https://huggingface.co/charlesnovak/InstructPro2) |
|
|
| ## Contents |
|
|
| ``` |
| stage1/ instruction-following pretraining |
| train.jsonl valid.jsonl test.jsonl |
| ligand/ ligand-binding protein design |
| train.jsonl valid.jsonl test_ligand.jsonl test_ligand_unseen.jsonl |
| antibody/ antibody HCDR design |
| train.jsonl valid.jsonl final_antibody_test.jsonl |
| eval_structures/ ground-truth structures for Stage 1 evaluation |
| cluster_<id>/ predicted structures for one cluster |
| targets.csv row -> cluster manifest |
| ``` |
|
|
| | Split | Rows | | |
| | --- | ---: | --- | |
| | `stage1/train` | 200,000 | sample of 205,727,986 | |
| | `stage1/valid` | 1,000 | complete | |
| | `stage1/test` | 1,000 | complete | |
| | `ligand/train` | 50,000 | sample of 8,727,514 | |
| | `ligand/valid` | 3,263 | complete | |
| | `ligand/test_ligand` | 1,500 | complete | |
| | `ligand/test_ligand_unseen` | 127 | complete; held-out ligands | |
| | `antibody/train` | 21,384 | complete | |
| | `antibody/valid` | 433 | complete | |
| | `antibody/final_antibody_test` | 444 | complete | |
|
|
| The two Stage 1 and ligand training splits are samples of much larger |
| datasets, the full sets are available on request. Every other split is |
| complete. |
|
|
| About a third of the antibody training rows (7,068 of 21,384) carry an |
| antigen. The rest are heavy chains without a paired structure, and train |
| the infilling behaviour alone. |
|
|
| Every row is one JSON object per line: |
|
|
| | Field | Present in | | |
| | --- | --- | --- | |
| | `instruction` | stage1 | the natural-language design request | |
| | `text` | ligand, antibody | the same, under a different name | |
| | `tokenized_instruction` | all | the request as PubMedBERT token ids, which is what the model reads | |
| | `output` | stage1 | the reference protein | |
| | `target_protein` | ligand, antibody | the same, under a different name | |
| | `ligand` | ligand | SMILES strings | |
| | `antigen` | antibody | antigen chain sequences | |
| | `task_type` | stage1 | `autoregressive` or `infilling` | |
| | `modality_type` | ligand, antibody | `ligand` or `antibody` | |
| | `metadata` | all | provenance: UniProt or PDB id, InterPro annotations, cluster id, CDR positions | |
|
|
| A target may contain `<SEP>` and `<M1>`–`<M30>`, which mark spans to |
| infill: the scaffold precedes the separator and the spans that fill it |
| follow. Antibody targets use this to mark HCDR1-3 in a heavy chain. |
|
|
| ## Evaluation structures |
|
|
| `eval_structures/` holds the ground truth for Stage 1 scoring: 1,000 |
| cluster folders, each with the structures of related proteins, and |
| `targets.csv` mapping each test row to its cluster. A design is scored |
| against every member of the cluster, and the best match is reported. |
|
|
| Ground-truth structures for the ligand and antibody tasks are not included. |
|
|
| ## Usage |
|
|
| ```bash |
| huggingface-cli download charlesnovak/InstructPro2Bench \ |
| --repo-type dataset --local-dir $WORK_ROOT/data |
| ``` |
|
|
| The [code repository](https://github.com/chovak/InstructPro2) reads this |
| layout directly. To design proteins from your own descriptions instead, see |
| its README — you do not need this dataset for that. |
|
|
| ## Notes |
|
|
| Proteins longer than 1,024 tokens are outside the model's context and are |
| skipped at generation time, so some test rows produce no design and have no |
| ground-truth structure. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{instructpro2, |
| title = {InstructPro2: Natural Language and Biological Grounding Guided De Novo Protein Design}, |
| author = {Charles Novak and Zhenqiao Song and Chuan Li and Jianwen Xie and Lei Li}, |
| journal = {arXiv preprint arXiv:2610.19592}, |
| year = {2026} |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT. |
