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

# Mol2Pro-base

## Model description

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


- **Code:** https://github.com/contributor-anonymous/Mol2Pro-tools 
- **Training data** https://huggingface.co/datasets/contributor-anonymous/Mol2Pro-Binder-Dataset



## How to use

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

model_id = "contributor-anonymous/Mol2Pro-base"
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")

# 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.