| --- |
| 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 reactants of reactions. You can use the demo [here](https://huggingface.co/spaces/sagawa/ReactionT5_task_retrosynthesis). |
|
|
|
|
| ### Model Sources |
|
|
| <!-- Provide the basic links for the model. --> |
|
|
| - **Repository:** https://github.com/sagawatatsuya/ReactionT5v2 |
| - **Paper:** https://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01075-4 |
| - **Demo:** https://huggingface.co/spaces/sagawa/ReactionT5 |
|
|
| ## Uses |
|
|
| <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
| 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. |
|
|
| ```python |
| 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 |
|
|
| <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
| We used the [Open Reaction Database (ORD) dataset](https://drive.google.com/file/d/1JozA2OlByfZ-ILt5H5YrTjLJvSvD8xdL/view?usp=drive_link) for model training. In addition, we used [USPTO_50k dataset](https://yzhang.hpc.nyu.edu/T5Chem/index.html)'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. |
|
|
| ```python |
| 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)](https://huggingface.co/sagawa/ReactionT5v2-retrosynthesis) | - | USPTO_50k | 13.8 | 18.6 | 21.4 | 26.2 | |
| | [ReactionT5](https://huggingface.co/sagawa/ReactionT5v2-retrosynthesis-USPTO_50k) | 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 |
| |
| <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
| ``` |
| @article{Sagawa2025, |
| title = {ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data}, |
| author = {Sagawa, Tatsuya and Kojima, Ryosuke}, |
| journal = {Journal of Cheminformatics}, |
| year = {2025}, |
| volume = {17}, |
| number = {1}, |
| pages = {126}, |
| doi = {10.1186/s13321-025-01075-4}, |
| url = {https://doi.org/10.1186/s13321-025-01075-4} |
| } |
| ``` |
