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Model Card for ReactionT5v2-retrosynthesis

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

Model Sources

Uses

You can use this model for retrosynthesis 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-retrosynthesis", return_tensors="pt")
model = AutoModelForSeq2SeqLM.from_pretrained("sagawa/ReactionT5v2-retrosynthesis")

inp = tokenizer('CCN(CC)CCNC(=S)NC1CCCc2cc(C)cnc21', 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 # 'CCN(CC)CCN=C=S.Cc1cnc2c(c1)CCCC2N'

Training Details

Training Procedure

We used the Open Reaction Database (ORD) dataset for model training. In addition, we used USPTO_50k 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_retrosynthesis
python train.py \
    --output_dir='t5' \
    --epochs=80 \
    --lr=2e-4 \
    --batch_size=32 \
    --input_max_len=100 \
    --target_max_len=150 \
    --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_50k/test.csv' \
    --pretrained_model_name_or_path='sagawa/CompoundT5'

Results

Model Training set Test set Top-1 [% acc.] Top-2 [% acc.] Top-3 [% acc.] Top-5 [% acc.]
Sequence-to-sequence USPTO_50k USPTO_50k 37.4 - 52.4 57.0
Molecular Transformer USPTO_50k USPTO_50k 43.5 - 60.5 -
SCROP USPTO_50k USPTO_50k 43.7 - 60.0 65.2
T5Chem USPTO_50k USPTO_50k 46.5 - 64.4 70.5
CompoundT5 USPTO_50k USPTO_50k 44,2 55.2 61.4 67.3
ReactionT5 (This model) - USPTO_50k 13.8 18.6 21.4 26.2
ReactionT5 USPTO_50k USPTO_50k 71.2 81.4 84.9 88.2

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

Citation

arxiv link: https://arxiv.org/abs/2311.06708

@misc{sagawa2023reactiont5,  
      title={ReactionT5: a large-scale pre-trained model towards application of limited reaction data}, 
      author={Tatsuya Sagawa and Ryosuke Kojima},  
      year={2023},  
      eprint={2311.06708},  
      archivePrefix={arXiv},  
      primaryClass={physics.chem-ph}  
}
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