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

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 = "contributor-anonymous/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)