Ersilia Model Hub Identifier: eos238c
6.1K
Assigns a molecule to twelve broad therapeutic categories taken from the MeSH therapeutic use vocabulary, inferring likely clinical purpose straight from structure. Meyer and colleagues gathered 6955 PubChem compounds each falling in exactly one of the twelve classes, reaching 83 to 88 percent accuracy with convolutional networks on molecule images and random forests on Morgan fingerprints. Ersilia replicates the task with twelve independent binary classifiers, so several categories can score highly for one compound even though the training labels were exclusive.
This model was incorporated on 2022-10-12. Last packaged on 2026-11-06.
eos238cmesh-therapeutic-useAnnotationActivity predictionAnyAnyTherapeutic indicationCompound112FixedBelow are the Output Columns of the model:
| Name | Type | Direction | Description |
|---|---|---|---|
| antiinfective_rank | float | high | Rank score of how likely the compound is to belong to the anti-infective therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| antiinflammatory_rank | float | high | Rank score of how likely the compound is to belong to the anti-inflammatory therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| antineoplastic_rank | float | high | Rank score of how likely the compound is to belong to the antineoplastic therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| cardio_rank | float | high | Rank score of how likely the compound is to belong to the cardiovascular therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| cns_rank | float | high | Rank score of how likely the compound is to belong to the central nervous system therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| dermatologic_rank | float | high | Rank score of how likely the compound is to belong to the dermatologic therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| gastrointestinal_rank | float | high | Rank score of how likely the compound is to belong to the gastrointestinal therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| hematologic_rank | float | high | Rank score of how likely the compound is to belong to the hematologic therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| lipidregulating_rank | float | high | Rank score of how likely the compound is to belong to the lipid regulating therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
| reproductivecontrol_rank | float | high | Rank score of how likely the compound is to belong to the reproductive control therapeutic use class as defined by MeSH. Calibrated against 50K compounds from the Ersilia reference library so that a score of 0.65 is better than 99% of them. Recommended threshold: 0.65. |
10 of 12 columns are shown
LocalReplicatedAMD64, ARM6423874953607.18Computational Performance (seconds):
44.9138.171089.82Peer reviewed2019This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a GPL-3.0-only 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 eos238c
Then, you can serve, run and close the model as follows:
# serve the model
ersilia serve eos238c
# 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
sha256:267466dad…
Size
3.5 GB
Last updated
about 9 hours ago
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