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, which designs proteins from natural-language function descriptions.
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
huggingface-cli download charlesnovak/InstructPro2Bench \
--repo-type dataset --local-dir $WORK_ROOT/data
The code repository 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
@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.