ersiliaos/eos6m2k repository overview

⁠MolE antimicrobial potential

A publicly available dataset accounting for the effect of 1,197 marketed drugs against 40 bacterial strains was used to train an XGBoost model, termed MolE-XGBoost, that predicts growth inhibition using MolE pre-trained representations. The model enabled a concise assessment of the antimicrobial potential of chemical compounds, including the re-discovery of de novo structurally distinct antibiotic candidates and the identification of broad-spectrum activity in other compounds that would have been missed by standard models.

This model was incorporated on 2025-08-21.Last packaged on 2026-07-07.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos6m2k
• Slug: mole-antimicrobial

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Antimicrobial resistance
• Target Organism: Akkermansia muciniphila, Bacteroides caccae, Bacteroides fragilis, Bacteroides ovatus, Bacteroides thetaiotaomicron, Bacteroides uniformis, Bacteroides vulgatus, Bacteroides xylanisolvens, Bifidobacterium adolescentis, Bifidobacterium longum, Bilophila wadsworthia, Blautia obeum, Clostridium bolteae, Clostridium difficile, Clostridium perfringens, Clostridium ramosum, Clostridium saccharolyticum, Collinsella aerofaciens, Coprococcus comes, Dorea formicigenerans, Eggerthella lenta, Escherichia coli, Eubacterium eligens, Eubacterium rectale, Fusobacterium nucleatum, Lactobacillus paracasei, Odoribacter splanchnicus, Parabacteroides distasonis, Parabacteroides merdae, Prevotella copri, Roseburia hominis, Roseburia intestinalis, Ruminococcus bromii, Ruminococcus gnavus, Ruminococcus torques, Streptococcus parasanguinis, Streptococcus salivarius, Veillonella parvula
• Tags: Antimicrobial activity

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 43
• Output Consistency: Fixed
• Interpretation: Growth inhibition probability prediction of 40 bacterial strains.

Below are the Output Columns of the model:

10 of 43 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos6m2k⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos6m2k.zip⁠

⁠Resource Consumption

• Model Size (Mb): 794
• Environment Size (Mb): 2245
• Image Size (Mb): 4621.16

Computational Performance (seconds):

• 10 inputs: 38.39
• 100 inputs: 23.84
• 10000 inputs: 343.86

⁠References

• Source Code: https://github.com/rolayoalarcon/mole_antimicrobial_potential⁠
• Publication: https://doi.org/10.1038/s41467-025-58804-4⁠
• Publication Type: Peer reviewed
• Publication Year: 2025
• Ersilia Contributor: arnaucoma24⁠

⁠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 eos6m2k

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

# serve the model
ersilia serve eos6m2k
# 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!