Ersilia Model Hub Identifier: eos88ir
165
Embeds a molecule as an 8192-dimensional vector from its SMILES alone. The authors fused six views of each molecule (descriptors, graph, image, bioactivity, quantum and language features) into a teacher embedding and trained a Mamba state-space model on about 2.2 million ChEMBL compounds to predict it. A classifier built on the embedding prioritised two compounds that reduced hydroxyurea-induced DNA damage in yeast. Ersilia canonicalises SMILES first; inputs beyond 126 tokens are truncated.
This model was incorporated on 2026-10-01.Last packaged on 2026-10-02.
eos88irchemical-dice-embeddingsRepresentationFeaturizationAnyAnyEmbedding, Chemical language model, ChEMBLCompound18192FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| feat_0000 | float | Chemical Dice embedding dimension 0 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0001 | float | Chemical Dice embedding dimension 1 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0002 | float | Chemical Dice embedding dimension 2 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0003 | float | Chemical Dice embedding dimension 3 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0004 | float | Chemical Dice embedding dimension 4 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0005 | float | Chemical Dice embedding dimension 5 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0006 | float | Chemical Dice embedding dimension 6 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0007 | float | Chemical Dice embedding dimension 7 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0008 | float | Chemical Dice embedding dimension 8 of 8192 predicted from the SMILES by the distilled Mamba model | |
| feat_0009 | float | Chemical Dice embedding dimension 9 of 8192 predicted from the SMILES by the distilled Mamba model |
10 of 8192 columns are shown
LocalExternalAMD64, ARM6438915172251.54Computational Performance (seconds):
34.69111.95-1Peer reviewed2026This 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.
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 eos88ir
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos88ir
# 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
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Content type
Image
Digest
sha256:69fe5ba23…
Size
1.1 GB
Last updated
1 day ago
docker pull ersiliaos/eos88ir