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.
eos9q2imol-jepaRepresentationFeaturizationAnyAnyEmbeddingCompound1512FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| feat_000 | float | embedding feature 0 | |
| feat_001 | float | embedding feature 1 | |
| feat_002 | float | embedding feature 2 | |
| feat_003 | float | embedding feature 3 | |
| feat_004 | float | embedding feature 4 | |
| feat_005 | float | embedding feature 5 | |
| feat_006 | float | embedding feature 6 | |
| feat_007 | float | embedding feature 7 | |
| feat_008 | float | embedding feature 8 | |
| feat_009 | float | embedding feature 9 |
10 of 512 columns are shown
LocalExternalAMD64, ARM6417480928307.95Computational Performance (seconds):
38.1422.4316.69Preprint2026This 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.
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
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!
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.
eos9q2imol-jepaRepresentationFeaturizationAnyAnyEmbeddingCompound1512FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| feat_000 | float | embedding feature 0 | |
| feat_001 | float | embedding feature 1 | |
| feat_002 | float | embedding feature 2 | |
| feat_003 | float | embedding feature 3 | |
| feat_004 | float | embedding feature 4 | |
| feat_005 | float | embedding feature 5 | |
| feat_006 | float | embedding feature 6 | |
| feat_007 | float | embedding feature 7 | |
| feat_008 | float | embedding feature 8 | |
| feat_009 | float | embedding feature 9 |
10 of 512 columns are shown
LocalExternalAMD64, ARM6417480928307.95Computational Performance (seconds):
38.1422.4316.69Preprint2026This 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.
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
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!