ersiliaos/eos7ye0 repository overview

⁠ChemFH chemical frequent hitter detection

ChemFH is an integrated model for the detection of frequent hitters in chemical screening across various parameters, including colloidal aggregates, firefly luciferase reporter enzyme inhibition, fluorescence, chemical reactivity, and promiscuity, as well as classical PAINS alerts and others. ChemFH contains ChemProp models as well as rule-based filters based on liable substructures.

This model was incorporated on 2025-08-23.Last packaged on 2026-07-06.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos7ye0
• Slug: chemfh

⁠Domain

• Task: Annotation
• Subtask: Property calculation or prediction
• Biomedical Area: Any
• Target Organism: Any
• Tags: Frequent hitter

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 17
• Output Consistency: Fixed
• Interpretation: Probability scores range from 0 to 1, with higher values indicating a greater likelihood of being a frequent hitter. A threshold of 0.5 is commonly used to classify compounds as frequent hitters (scores >= 0.5) or non-frequent hitters (scores < 0.5).

Below are the Output Columns of the model:

10 of 17 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos7ye0⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7ye0.zip⁠

⁠Resource Consumption

• Model Size (Mb): 298
• Environment Size (Mb): 1940
• Image Size (Mb): 2149.39

Computational Performance (seconds):

• 10 inputs: 32.91
• 100 inputs: 36.35
• 10000 inputs: 923.07

⁠References

• Source Code: https://github.com/antwiser/ChemFH⁠
• Publication: https://doi.org/10.1093/nar/gkae495⁠
• Publication Type: Peer reviewed
• Publication Year: 2025
• Ersilia Contributor: miquelduranfrigola⁠

⁠License

This 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.

⁠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 eos7ye0

Then, you can serve, run and close the model as follows:

# serve the model
ersilia serve eos7ye0
# 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!