ersiliaos/eos6aun repository overview

⁠RXNFP - chemical reaction fingerprints

Encodes a whole chemical reaction, rather than a single molecule, as 256 features read from a BERT model trained on reaction SMILES. Schwaller and colleagues fine-tuned it to assign reaction classes, reaching 98.9% accuracy, and found that mechanistically related transformations end up close together in the learned space. This lets chemists retrieve nearest-neighbour reactions and map reaction space without annotating reaction centres or separating reactants from reagents. A complete reaction string is required, so the fingerprint cannot be computed from a product alone.

This model was incorporated on 2023-03-28.Last packaged on 2026-10-13.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos6aun
• Slug: rxn-fingerprint

⁠Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Fingerprint, Embedding, Chemical synthesis

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 256
• Output Consistency: Fixed
• Interpretation: 256 features encoding a chemical reaction from an attention-based reaction transformer.

Below are the Output Columns of the model:

10 of 256 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos6aun⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos6aun.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 6112
• Image Size (Mb): 6095.72

Computational Performance (seconds):

• 10 inputs: 25.73
• 100 inputs: 18.03
• 10000 inputs: 262.22

⁠References

• Source Code: https://github.com/rxn4chemistry/rxnfp/tree/master/⁠
• Publication: https://doi.org/10.1038/s42256-020-00284-w⁠
• Publication Type: Peer reviewed
• Publication Year: 2021
• Ersilia Contributor: samuelmaina⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a MIT⁠ license.

Notice: Ersilia grants access to models as is, directly from the original authors, please refer to the original code repository and/or publication if you use the model in your research.

⁠Use

To use this model locally, you need to have the Ersilia CLI⁠ installed. The model can be fetched using the following command:

# fetch model from the Ersilia Model Hub
ersilia fetch eos6aun

Then, you can serve, run and close the model as follows:

# serve the model
ersilia serve eos6aun
# generate an example file
ersilia example -n 3 -f my_input.csv
# run the model
ersilia run -i my_input.csv -o my_output.csv
# close the model
ersilia close

⁠About Ersilia

The Ersilia Open Source Initiative⁠ is a tech non-profit organization fueling sustainable research in the Global South. Please cite⁠ the Ersilia Model Hub if you've found this model to be useful. Always let us know⁠ if you experience any issues while trying to run it. If you want to contribute to our mission, consider donating⁠ to Ersilia!