README.md

metadata

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

Model Card for ReactionT5v2-forward

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

Uses

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

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

cd task_forward
python finetune.py \
    --output_dir='t5' \
    --epochs=50 \
    --lr=2e-5 \
    --batch_size=32 \
    --input_max_len=200 \
    --target_max_len=150 \
    --evaluation_strategy='epoch' \
    --save_strategy='epoch' \
    --logging_strategy='epoch' \
    --save_total_limit=10 \
    --train_data_path='../data/USPTO_MIT/MIT_separated/train.csv' \
    --valid_data_path='../data/USPTO_MIT/MIT_separated/val.csv' \
    --disable_tqdm \
    --model_name_or_path='sagawa/ReactionT5v2-forward'

Results

Model	Training set	Test set	Top-1 [% acc.]	Top-2 [% acc.]	Top-3 [% acc.]	Top-5 [% acc.]
Sequence-to-sequence	USPTO_MIT	USPTO_MIT	80.3	84.7	86.2	87.5
WLDN	USPTO_MIT	USPTO_MIT	80.6 (85.6)	90.5	92.8	93.4
Molecular Transformer	USPTO_MIT	USPTO_MIT	88.8	92.6	–	94.4
T5Chem	USPTO_MIT	USPTO_MIT	90.4	94.2	–	96.4
CompoundT5	USPTO_MIT	USPTO_MIT	86.6	89.5	90.4	91.2
ReactionT5	-	USPTO_MIT	92.8	95.6	96.4	97.1
ReactionT5 (This model)	USPTO_MIT	USPTO_MIT	97.5	98.6	98.8	99.0

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}  
}