ersiliaos/eos5g6m repository overview

⁠GLACIER Molecular Embeddings

GLACIER encodes molecules into 512 features using a student-teacher arrangement in which a lightweight student learns to reproduce representations from larger multimodal teachers. Nguyen and colleagues designed it so that the expressive power of heavy foundation models becomes available at a fraction of the inference cost, with the student trained to match teacher embeddings rather than to predict properties. The embedding is task-independent, and its dimensions carry no interpretable chemical meaning individually.

This model was incorporated on 2026-08-03.Last packaged on 2026-10-05.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos5g6m
• Slug: glacier-embeddings

⁠Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Descriptor, Embedding, Chemical graph model

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 512
• Output Consistency: Fixed
• Interpretation: 512 features encoding molecular structure from a student-teacher foundation model.

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/eos5g6m⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos5g6m.zip⁠

⁠Resource Consumption

• Model Size (Mb): 26
• Environment Size (Mb): 1867
• Image Size (Mb): 1857.13

Computational Performance (seconds):

• 10 inputs: 34.22
• 100 inputs: 24.72
• 10000 inputs: 390.63

⁠References

• Source Code: https://github.com/eemokey/glacier⁠
• Publication: https://doi.org/10.1145/3770855.3819032⁠
• Publication Type: Peer reviewed
• Publication Year: 2026
• Ersilia Contributor: TiagoJanela⁠

⁠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 eos5g6m

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

# serve the model
ersilia serve eos5g6m
# 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!