Instructions to use Aurigene-AI/ReactionT5v2-retrosynthesis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aurigene-AI/ReactionT5v2-retrosynthesis with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Aurigene-AI/ReactionT5v2-retrosynthesis")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Aurigene-AI/ReactionT5v2-retrosynthesis")
model = AutoModelForSeq2SeqLM.from_pretrained("Aurigene-AI/ReactionT5v2-retrosynthesis", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Aurigene-AI/ReactionT5v2-retrosynthesis with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aurigene-AI/ReactionT5v2-retrosynthesis"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aurigene-AI/ReactionT5v2-retrosynthesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Aurigene-AI/ReactionT5v2-retrosynthesis

SGLang

How to use Aurigene-AI/ReactionT5v2-retrosynthesis with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Aurigene-AI/ReactionT5v2-retrosynthesis" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aurigene-AI/ReactionT5v2-retrosynthesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Aurigene-AI/ReactionT5v2-retrosynthesis" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aurigene-AI/ReactionT5v2-retrosynthesis",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Aurigene-AI/ReactionT5v2-retrosynthesis with Docker Model Runner:
```
docker model run hf.co/Aurigene-AI/ReactionT5v2-retrosynthesis
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Mirrored by Aurigene AI

Discovery stage: Synthesis planning

Single-step retrosynthesis: product SMILES in, plausible reactants out. Chain it with the forward model to score a proposed route.

Upstream: sagawa/ReactionT5v2-retrosynthesis - all credit to the original authors; the model card and licence below are theirs.

Explore the rest of the catalogue: Molecule Explorer - Protein Target Explorer - Drug Discovery Model Hub

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://jcheminf.biomedcentral.com/articles/10.1186/s13321-025-01075-4
Demo: https://huggingface.co/spaces/sagawa/ReactionT5

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

@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}
}

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F32

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Generative Chemistry & Synthesis Planning

Collection

De novo molecule generation, forward reaction prediction, retrosynthesis and molecule captioning. • 5 items • Updated about 16 hours ago