README.md

---
language: en
library_name: transformers
pipeline_tag: text-generation
tags:
- t5
- molecule-to-protein
- smiles
- protein-generation
- binder
- ligand
license: apache-2.0
datasets:
- AI4PD/Mol2Pro-Binder-Dataset
---

# Mol2Pro-base

## Model description

- **Architecture:** T5-efficient-base https://huggingface.co/google/t5-efficient-base
- **Tokenization:** https://huggingface.co/AI4PD/Mol2Pro-tokenizer


- **Code:** https://github.com/AI4PDLab/Mol2Pro  
- **Training data** https://huggingface.co/datasets/AI4PD/Mol2Pro-Binder-Dataset
- **Paper:** https://doi.org/10.64898/2026.02.06.704305



## How to use

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_id = "AI4PD/Mol2Pro-base"
tokenizer_id = "AI4PD/Mol2Pro-tokenizer"

# Load tokenizers
tokenizer_mol = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="smiles")
tokenizer_aa  = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="aa")

# Load model
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
```

## Intended use
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.

## Citation

If you find this work useful, please cite:

```bibtex
@article{VicenteSola2026Generalise,
  title   = {Generalise or Memorise? Benchmarking Ligand-Conditioned Protein Generation from Sequence-Only Data},
  author  = {Vicente-Sola, Alex and Dornfeld, Lars and Coines, Joan and Ferruz, Noelia},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.02.06.704305},
}