AI4PD / Mol2Pro-tokenizer

Mol2Pro-tokenizer

Paper: Generalise or Memorise? Benchmarking Ligand-Conditioned Protein Generation from Sequence-Only Data

Tokenizer description

This repository provides the paired tokenizers used by Mol2Pro models:

• smiles/ : tokenizer for molecule inputs (SMILES) used on the encoder side.
• aa/ : tokenizer for protein sequence outputs used on the decoder side.

The two tokenizers are designed to be used together with the Mol2Pro sequence-to-sequence checkpoints (see the model card: AI4PD/Mol2Pro-base ).

Offset vocabulary

Mol2Pro uses an offset token-id scheme so that SMILES tokens and amino-acid tokens do not collide in id space. Avoids sharing embeddings for identical token strings.

• The AA tokenizer uses its natural token id space.
• The SMILES tokenizer vocabulary ids are offset above the AA vocabulary ids.

How to use

from transformers import AutoTokenizer

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")

# Example:
smiles = "CCO"
enc = tokenizer_mol(smiles, return_tensors="pt")
print("Encoder token ids:", enc.input_ids[0].tolist())
print("Encoder tokens:", tokenizer_mol.convert_ids_to_tokens(enc.input_ids[0]))

aa_text = tokenizer_aa.decode([0, 1, 2], skip_special_tokens=True)
print("Decoded protein sequence:", decoded)

Citation

If you find this work useful, please cite:

@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},
}