ersiliaos/eos2mhp repository overview

⁠BACE-1 inhibition

Predicts inhibition of beta-secretase 1, the neuronal protease that cleaves amyloid precursor protein and has been pursued for years as an Alzheimers disease target. Fine-tuning used the MoleculeNet BACE set, 1,513 compounds reported as human BACE-1 inhibitors with binary activity labels, on top of a self-supervised graph transformer pretrained on 10 million ChEMBL and ZINC15 molecules; three fine-tuned folds are averaged at inference. Because the data centre on a few congeneric series, generalisation to unrelated scaffolds is uncertain.

This model was incorporated on 2022-07-13. Last packaged on 2026-11-03.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos2mhp
• Slug: grover-bace

⁠Domain

• Task: Annotation
• Subtask: Activity prediction
• Biomedical Area: Alzheimer
• Target Organism: Homo sapiens
• Tags: BACE, MoleculeNet, Chemical graph model

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: Probability of BACE-1 inhibition, with actives defined at a 0.1 uM cut-off.

Below are the Output Columns of the model:

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://chstno1.isgood.host/r/ersiliaos/eos2mhp⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2mhp.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1378
• Environment Size (Mb): 2406
• Image Size (Mb): 3756.1

Computational Performance (seconds):

• 10 inputs: 30.3
• 100 inputs: 56.28
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/tencent-ailab/grover⁠
• Publication: https://doi.org/10.48550/arXiv.2007.02835⁠
• Publication Type: Preprint
• Publication Year: 2020
• 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 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 eos2mhp

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

# serve the model
ersilia serve eos2mhp
# 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!