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
- Repository: https://github.com/sagawatatsuya/ReactionT5v2
- Paper: https://arxiv.org/abs/2311.06708
- Demo: https://huggingface.co/spaces/sagawa/ReactionT5_task_retrosynthesis
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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