ReactionT5v2-forward

Model Card for ReactionT5v2-forward

This is a ReactionT5 pre-trained to predict the products of reactions. You can use the demo here.

Model Sources

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• Repository: https://github.com/sagawatatsuya/ReactionT5v2
• Paper: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01075-4
• Demo: https://huggingface.co/spaces/sagawa/ReactionT5

Uses

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You can use this model for forward reaction prediction or fine-tune this model with your dataset.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sagawa/ReactionT5v2-forward", return_tensors="pt")
model = AutoModelForSeq2SeqLM.from_pretrained("sagawa/ReactionT5v2-forward")

inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
output = model.generate(**inp, num_beams=1, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
output = tokenizer.decode(output['sequences'][0], skip_special_tokens=True).replace(' ', '').rstrip('.')
output # 'CN1CCC=C(CO)C1'

Training Details

Training Procedure

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We used the Open Reaction Database (ORD) dataset for model training. In addition, we used USPTO_MIT dataset's test split to prevent data leakage. The command used for training is the following. For more information about data preprocessing and training, please refer to the paper and GitHub repository.

cd task_forward
python train.py \
    --output_dir='t5' \
    --epochs=100 \
    --lr=1e-3 \
    --batch_size=32 \
    --input_max_len=150 \
    --target_max_len=100 \
    --weight_decay=0.01 \
    --evaluation_strategy='epoch' \
    --save_strategy='epoch' \
    --logging_strategy='epoch' \
    --train_data_path='../data/preprocessed_ord_train.csv' \
    --valid_data_path='../data/preprocessed_ord_valid.csv' \
    --test_data_path='../data/preprocessed_ord_test.csv' \
    --USPTO_test_data_path='../data/USPTO_MIT/MIT_separated/test.csv' \
    --disable_tqdm \
    --pretrained_model_name_or_path='sagawa/CompoundT5'

Results

Performance comparison of Compound T5, ReactionT5, and other models in product prediction.

Citation

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@article{Sagawa2025,
  title   = {ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data},
  author  = {Sagawa, Tatsuya and Kojima, Ryosuke},
  journal = {Journal of Cheminformatics},
  year    = {2025},
  volume  = {17},
  number  = {1},
  pages   = {126},
  doi     = {10.1186/s13321-025-01075-4},
  url     = {https://doi.org/10.1186/s13321-025-01075-4}
}