retrochimera 1.2.0

Frontier retrosynthesis prediction model

RetroChimera

Backed by Syntheseus • Paper

RetroChimera is a frontier retrosynthesis model, built upon ensembling two novel components with complementary inductive biases. It outperforms existing models by a large margin, can learn from a very small number of examples per reaction class, and is preferred by industrial organic chemists over the reactions it was trained on in blind tests.

Using RetroChimera

To install retrochimera locally, run

conda env create -f environment.yml
conda activate retrochimera

pip install retrochimera

then you can run inference via

from retrochimera import RetroChimeraModel
from syntheseus import Molecule

model = RetroChimeraModel(model_dir="/model/checkpoint/dir/")
mol = Molecule("Oc1ccc(OCc2ccccc2)c(Br)c1")

predictions = model([mol], num_results=3)

for p in predictions[0]:
    print(p, f"({100. * p.metadata['probability']:.2f}%)")

For installation, there are two additional dependency groups: dev for running tests, and graphium for building the model architecture we used for USPTO-50K; if you care about running the USPTO-50K checkpoint, you need to install via pip install retrochimera[graphium].

If you want to train your own checkpoint, please follow the instructions in retrochimera/README.md.

Checkpoints for RetroChimera 1

The main (and most powerful) checkpoint we release is trained on Pistachio. For benchmarking, we also provide (weaker) checkpoints trained on USPTO-50K and USPTO-FULL.

If you care about reproducing the USPTO-* results from our paper exactly, make sure to use the inference hyperparameters listed in Extended Data Tables 3 and 4. By default, these parameters are set to values optimal for the Pistachio checkpoint.

[!WARNING] RetroChimera 1 is being released for research and experimentation - we hope you try to break it in any way possible and share the results back to us. As any ML model it is not free from errors and may hallucinate, in particular when used for inputs out of the training distribution. We look forward to collaborating with the community so we can improve the model for everyone!

Before using any of the predictions in a real-world setting, they must be risk-assessed and verified independently by chemistry experts. In particular, reactions ranked lower in the output list are increasingly likely to be hallucinations; we recommend requesting no more than 5-10 reactions per input unless paired with stringent filtering (see e.g. [1][2])

If you find that RetroChimera 1 doesn't work on your favourite drug-like molecule, please let us know at [e-mail], so we can make sure we improve this in the next model version.

Citation

If you use RetroChimera in your work, please consider citing our arXiv preprint (bibtex below).

@article{maziarz2025chemist,
  title={Chemist-aligned retrosynthesis by ensembling diverse inductive bias models},
  author={Maziarz, Krzysztof and Liu, Guoqing and Misztela, Hubert and Tripp, Austin and Li, Junren and Kornev, Aleksei and Gai{\'n}ski, Piotr and Hoefling, Holger and Fortunato, Mike and Gupta, Rishi and Segler, Marwin},
  journal={arXiv preprint arXiv:2412.05269},
  year={2025}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [e-mail] with any additional questions or comments.

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