metadata

language:
  - en
license: mit
tags:
  - chemistry
  - SMILES
  - retrosynthesis
datasets:
  - ORD
metrics:
  - accuracy

Model Card for ReactionT5v2-retrosynthesis

This is a ReactionT5 pre-trained to predict the products of reactions and fine-tuned on USPOT_50k's train split. Base model before fine-tuning is 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-USPTO_50k", return_tensors="pt")
model = AutoModelForSeq2SeqLM.from_pretrained("sagawa/ReactionT5v2-retrosynthesis-USPTO_50k")

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. The command used for training is the following. For more information, please refer to the paper and GitHub repository.

python train_without_duplicates.py \
    --model='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='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_train.csv' \
    --valid_data_path='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_valid.csv' \
    --test_data_path='/home/acf15718oa/ReactionT5_neword/data/all_ord_reaction_uniq_with_attr20240506_v3_test.csv' \
    --USPTO_test_data_path='/home/acf15718oa/ReactionT5_neword/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	-	USPTO_50k	13.8	18.6	21.4	26.2
ReactionT5 (This model)	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}  
}