ersiliaos/eos9o72 repository overview

⁠CheMeleon embeddings

CheMeleon encodes a molecule as 2,048 features with a directed message-passing neural network pretrained to reproduce deterministic Mordred descriptors for roughly a million PubChem molecules, a noise-free target rather than masked structure or experimental labels. Burns and colleagues at MIT reasoned that descriptors already carry low-noise chemical information, so predicting them gives a representation grounded in known chemistry while remaining learnable. It won most of 58 Polaris and MoleculeACE benchmarks but, like the models compared against it, still fails to resolve activity cliffs.

This model was incorporated on 2025-07-01.Last packaged on 2026-10-22.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9o72
• Slug: chemeleon

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 2048
• Output Consistency: Fixed
• Interpretation: 2048 features encoding molecular structure from a descriptor-pretrained foundation model.

Below are the Output Columns of the model:

10 of 2048 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9o72⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9o72.zip⁠

⁠Resource Consumption

• Model Size (Mb): 34
• Environment Size (Mb): 1777
• Image Size (Mb): 1763.97

Computational Performance (seconds):

• 10 inputs: 31.78
• 100 inputs: 24.88
• 10000 inputs: 362.29

⁠References

• Source Code: https://github.com/JacksonBurns/chemeleon/tree/main⁠
• Publication: https://doi.org/10.1021/acs.jcim.6c01546⁠
• Publication Type: Peer reviewed
• Publication Year: 2026
• Ersilia Contributor: arnaucoma24⁠

⁠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 eos9o72

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

# serve the model
ersilia serve eos9o72
# 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!