ersiliaos/eos96f4 repository overview

⁠Digitization of molecular complexity

Quantifies how complex a molecule appears to an experienced chemist, learned from expert judgements rather than defined by a formula. The authors framed this as learning to rank, training on pairwise complexity comparisons, and used Shapley analysis to identify which structural characteristics drive the assessments, among them ring fusion, stereocentres and unusual heteroatom arrangements. Because the target is human perception, the score reflects the intuitions of the chemists who supplied the rankings.

This model was incorporated on 2026-01-30.Last packaged on 2026-10-06.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos96f4
• Slug: digitization-complexity

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Molecular complexity score where higher values indicate a more complex compound.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos96f4⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos96f4.zip⁠

⁠Resource Consumption

• Model Size (Mb): 916
• Environment Size (Mb): 1377
• Image Size (Mb): 3002.28

Computational Performance (seconds):

• 10 inputs: 29.36
• 100 inputs: 20.49
• 10000 inputs: 236.55

⁠References

• Source Code: https://github.com/Ananikov-Lab/digitizing_molecular_complexity⁠
• Publication: https://doi.org/10.1039/D4SC07320G⁠
• Publication Type: Peer reviewed
• Publication Year: 2025
• Ersilia Contributor: miquelduranfrigola⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a CC-BY-4.0⁠ 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 eos96f4

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

# serve the model
ersilia serve eos96f4
# 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!