Ersilia Model Hub Identifier: eos1tt2
308
MolE, Recursions foundation model for molecular graphs, learns embeddings using a transformer with disentangled attention adapted from DeBERTa. Pretraining first predicts each atoms local environment in a self-supervised step, then refines embeddings on ~456,000 ChEMBL compounds across 1310 bioactivity tasks. The original study self-supervised on up to 842 million molecules and showed finetuned MolE topped 10 of 22 ADMET benchmarks from the Therapeutic Data Commons. Since Recursion never released those full weights, Ersilia instead serves the smaller GuacaMol/ChEMBL-pretrained checkpoint.
This model was incorporated on 2025-06-23.Last packaged on 2026-08-29.
eos1tt2mole-embeddingsRepresentationFeaturizationAnyAnyEmbedding, Chemical graph modelCompound1768FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| feat_000 | float | MolE embedding dimension 0 from the pretrained transformer's CLS token | |
| feat_001 | float | MolE embedding dimension 1 from the pretrained transformer's CLS token | |
| feat_002 | float | MolE embedding dimension 2 from the pretrained transformer's CLS token | |
| feat_003 | float | MolE embedding dimension 3 from the pretrained transformer's CLS token | |
| feat_004 | float | MolE embedding dimension 4 from the pretrained transformer's CLS token | |
| feat_005 | float | MolE embedding dimension 5 from the pretrained transformer's CLS token | |
| feat_006 | float | MolE embedding dimension 6 from the pretrained transformer's CLS token | |
| feat_007 | float | MolE embedding dimension 7 from the pretrained transformer's CLS token | |
| feat_008 | float | MolE embedding dimension 8 from the pretrained transformer's CLS token | |
| feat_009 | float | MolE embedding dimension 9 from the pretrained transformer's CLS token |
10 of 768 columns are shown
LocalExternalAMD6499626334556.49Computational Performance (seconds):
35.6340.08717.98Peer reviewed2024This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a Non-commercial 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 eos1tt2
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos1tt2
# 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
sha256:23bfcf820…
Size
2.6 GB
Last updated
about 1 month ago
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