Ersilia Model Hub Identifier: eos4ex3
7.1K
MolE representations (Molecular representation through redundancy reduced Embeddings) are task-independent, learned molecular embeddings generated through a self-supervised deep learning approach. They are designed to encode chemically meaningful information about molecules without needing labeled training data.
This model was incorporated on 2025-06-23.Last packaged on 2026-08-31.
eos4ex3mole-representationsRepresentationFeaturizationAnyAnyDescriptorCompound11000FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| feat_000 | float | Feature 0 of the Molecular representation through redundancy reduced Embeddings | |
| feat_001 | float | Feature 1 of the Molecular representation through redundancy reduced Embeddings | |
| feat_002 | float | Feature 2 of the Molecular representation through redundancy reduced Embeddings | |
| feat_003 | float | Feature 3 of the Molecular representation through redundancy reduced Embeddings | |
| feat_004 | float | Feature 4 of the Molecular representation through redundancy reduced Embeddings | |
| feat_005 | float | Feature 5 of the Molecular representation through redundancy reduced Embeddings | |
| feat_006 | float | Feature 6 of the Molecular representation through redundancy reduced Embeddings | |
| feat_007 | float | Feature 7 of the Molecular representation through redundancy reduced Embeddings | |
| feat_008 | float | Feature 8 of the Molecular representation through redundancy reduced Embeddings | |
| feat_009 | float | Feature 9 of the Molecular representation through redundancy reduced Embeddings |
10 of 1000 columns are shown
LocalExternalAMD64, ARM6476813343633.26Computational Performance (seconds):
40.3626.88303.91Peer reviewed2025This 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 eos4ex3
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos4ex3
# 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
sha256:0d2a8f2d5…
Size
2.5 GB
Last updated
about 1 month ago
docker pull ersiliaos/eos4ex3Pulls:
8
Sep 21 to Sep 27