ersiliaos/eos7pw8 repository overview

⁠Bayesian prediction of synthetic accessibility

Judges how readily a compound could be made, using SYBA, a Bernoulli naive Bayes scheme from Vorsilak and colleagues. Fragment log-odds come from 693,353 easy-to-synthesise molecules drawn from ZINC15 and an equal number of hard ones built by Nonpher, which morphs structures into deliberately awkward analogues. Fragments are ECFP8 environments assumed to be independent, so the score decomposes into per-fragment contributions, but it reflects the statistics of those reference sets rather than any route search.

This model was incorporated on 2021-10-25.Last packaged on 2026-10-08.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos7pw8
• Slug: syba-synthetic-accessibility

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Synthetic accessibility, Chemical synthesis

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Log-odds synthetic accessibility score, positive for easy-to-synthesise compounds and negative for hard-to-synthesise ones.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos7pw8⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7pw8.zip⁠

⁠Resource Consumption

• Model Size (Mb): 304
• Environment Size (Mb): 1046
• Image Size (Mb): 1277.77

Computational Performance (seconds):

• 10 inputs: 26.51
• 100 inputs: 16.64
• 10000 inputs: 27.27

⁠References

• Source Code: https://github.com/lich-uct/syba⁠
• Publication: https://doi.org/10.1186/s13321-020-00439-2⁠
• Publication Type: Peer reviewed
• Publication Year: 2020
• Ersilia Contributor: miquelduranfrigola⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a GPL-3.0-only⁠ 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 eos7pw8

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

# serve the model
ersilia serve eos7pw8
# 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!