ersiliaos/eos9q2i repository overview

⁠Mol-JEPA A multimodal Joint Embedding Predictive Architecture for Molecules

Multimodal joint-embedding predictive architecture that turns a SMILES string into a 512-dimensional embedding carrying predicted biological, phenotypic and quantum context, pretrained by modality masking over 4.69 M compounds.

This model was incorporated on 2026-09-02.Last packaged on 2026-10-14.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9q2i
• Slug: mol-jepa

⁠Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Embedding

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 512
• Output Consistency: Fixed
• Interpretation: The output of this template model should be interpreted like this.

Below are the Output Columns of the model:

10 of 512 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9q2i⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9q2i.zip⁠

⁠Resource Consumption

• Model Size (Mb): 174
• Environment Size (Mb): 8092
• Image Size (Mb): 8307.95

Computational Performance (seconds):

• 10 inputs: 38.14
• 100 inputs: 22.4
• 10000 inputs: 316.69

⁠References

• Source Code: https://github.com/Boehringer-Ingelheim/mol-jepa⁠
• Publication: https://arxiv.org/abs/2608.22642⁠
• Publication Type: Preprint
• Publication Year: 2026
• Ersilia Contributor: soufianeaatab⁠

⁠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 eos9q2i

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

# serve the model
ersilia serve eos9q2i
# 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!