chemistry SMILES product

Model Card for ReactionT5-product-prediction

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

Model Details

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Model Sources

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Uses

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How to Get Started with the Model

Download files and use the code below to get started with the model.

from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained('sagawa/ReactionT5-product-prediction')
inp = tokenizer('REACTANT:COC(=O)C1=CCCN(C)C1.O.[Al+3].[H-].[Li+].[Na+].[OH-]REAGENT:C1CCOC1', return_tensors='pt')
model = T5ForConditionalGeneration.from_pretrained('sagawa/ReactionT5-product-prediction')
output = model.generate(**inp, min_length=6, max_length=109, 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 # 'O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].O=S(=O)([O-])[O-].[Cr+3].[Cr+3]'

Training Details

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> We used Open Reaction Database (ORD) dataset for model training. Following is the command used for training. For more information, please refer to the paper and GitHub repository.

python train.py \
    --epochs=100 \
    --batch_size=32 \
    --data_path='../data/all_ord_reaction_uniq_with_attr_v3.csv' \
    --use_reconstructed_data \
    --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 USPTO 80.3 84.7 86.2 87.5
WLDN USPTO USPTO 80.6 (85.6) 90.5 92.8 93.4
Molecular Transformer USPTO USPTO 88.8 92.6 – 94.4
T5Chem USPTO USPTO 90.4 94.2 – 96.4
CompoundT5 USPTO USPTO 88.0 92.4 93.9 95.0
ReactionT5 ORD USPTO 0.0 <85.0> 0.0 <90.6> 0.0 <92.3> 0.0 <93.8>

Performance comparison of Compound T5, ReactionT5, and other models in product prediction. The values enclosed in ‘<>’ in the table represent the scores of the model that was fine-tuned on 200 reactions from the USPTO dataset. The score enclosed in ‘()’ is the one reported in the original paper.

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