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--- |
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language: en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- t5 |
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- molecule-to-protein |
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- smiles |
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- protein-generation |
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- binder |
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- ligand |
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license: apache-2.0 |
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datasets: |
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- AI4PD/Mol2Pro-Binder-Dataset |
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--- |
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# Mol2Pro-base |
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## Model description |
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- **Architecture:** T5-efficient-base https://huggingface.co/google/t5-efficient-base |
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- **Tokenization:** https://huggingface.co/AI4PD/Mol2Pro-tokenizer |
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- **Code:** https://github.com/AI4PDLab/Mol2Pro |
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- **Training data** https://huggingface.co/datasets/AI4PD/Mol2Pro-Binder-Dataset |
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- **Paper:** https://doi.org/10.64898/2026.02.06.704305 |
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## How to use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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import torch |
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model_id = "AI4PD/Mol2Pro-base" |
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tokenizer_id = "AI4PD/Mol2Pro-tokenizer" |
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# Load tokenizers |
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tokenizer_mol = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="smiles") |
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tokenizer_aa = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="aa") |
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# Load model |
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model = AutoModelForSeq2SeqLM.from_pretrained(model_id) |
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``` |
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## Intended use |
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Research use only. The model generates candidate sequences conditioned on small-molecule inputs; it does not guarantee binding or function and must be validated experimentally. |
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## Citation |
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If you find this work useful, please cite: |
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```bibtex |
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@article{VicenteSola2026Generalise, |
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title = {Generalise or Memorise? Benchmarking Ligand-Conditioned Protein Generation from Sequence-Only Data}, |
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author = {Vicente-Sola, Alex and Dornfeld, Lars and Coines, Joan and Ferruz, Noelia}, |
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journal = {bioRxiv}, |
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year = {2026}, |
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doi = {10.64898/2026.02.06.704305}, |
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} |
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