ersiliaos/eos3l5f repository overview

⁠CLAMP contrastive molecule pretraining embedding

CLAMP returns 768 features from a molecule encoder trained jointly with a language encoder, so structures and the text describing bioassays land in one shared space. Seidl and colleagues pretrained it contrastively on PubChem bioactivity, 2.1 million molecules measured across roughly 21,000 assays, which lets a new activity task be addressed from its written description alone with no task-specific retraining. The molecule side consumes an 8192-bit Morgan and descriptor fingerprint, and the embedding is meant to feed downstream models rather than be read dimension by dimension.

This model was incorporated on 2025-08-26. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos3l5f
• Slug: clamp

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 768
• Output Consistency: Fixed
• Interpretation: 768 features encoding molecular structure from a contrastive molecule-language model.

Below are the Output Columns of the model:

10 of 768 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos3l5f⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos3l5f.zip⁠

⁠Resource Consumption

• Model Size (Mb): 167
• Environment Size (Mb): 665
• Image Size (Mb): 783.75

Computational Performance (seconds):

• 10 inputs: 27.76
• 100 inputs: 18
• 10000 inputs: 73.01

⁠References

• Source Code: https://github.com/ml-jku/clamp⁠
• Publication: https://doi.org/10.48550/arXiv.2303.03363⁠
• Publication Type: Preprint
• Publication Year: 2023
• 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-or-later⁠ 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 eos3l5f

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

# serve the model
ersilia serve eos3l5f
# 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!