ersiliaos/eos238c repository overview

⁠MeSH therapeutic use based on chemical structure

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 2026-10-28.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos238c
• Slug: mesh-therapeutic-use

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Therapeutic indication

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 12
• Output Consistency: Fixed
• Interpretation: Probability of belonging to each of twelve MeSH therapeutic use classes, scored by twelve independent classifiers.

Below are the Output Columns of the model:

10 of 12 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: Replicated
• DockerHub: https://hub.docker.com/r/ersiliaos/eos238c⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos238c.zip⁠

⁠Resource Consumption

• Model Size (Mb): 106
• Environment Size (Mb): 7552
• Image Size (Mb): 7584

Computational Performance (seconds):

• 10 inputs: 70.76
• 100 inputs: 70.21
• 10000 inputs: 1602.39

⁠References

• Source Code: https://github.com/jgmeyerucsd/drug-class⁠
• Publication: https://doi.org/10.1021/acs.jcim.9b00236⁠
• Publication Type: Peer reviewed
• Publication Year: 2019
• Ersilia Contributor: Amna-28⁠

⁠License

This 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.

⁠Use

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

⁠About Ersilia

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!