Init model card

README.md

@@ -1,3 +1,63 @@
----
-license: apache-2.0
----
+---
+language: en
+library_name: transformers
+pipeline_tag: text2text-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},
+}
+