ersiliaos/eos9taz repository overview

⁠MoLeR scaffold completion with fragment seeds

Completes the Murcko scaffold of an input molecule with new chemistry using MoLeR, a graph-based variational autoencoder trained by Maziarz and colleagues on GuacaMols roughly 1.5 million drug-like molecules. Because the decoder assembles molecules from recurring structural motifs and is never conditioned on its own generation history, intermediate structures stay chemically valid and any scaffold can serve as a seed. Ersilia seeds the latent space with a different random draw of Enamine fragments at every run, drops duplicates and the input itself, and returns up to 100 molecules.

This model was incorporated on 2022-12-06.Last packaged on 2026-10-26.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9taz
• Slug: moler-enamine-fragments

⁠Domain

• Task: Sampling
• Subtask: Generation
• Biomedical Area: Any
• Target Organism: Any
• Tags: Chemical graph model, Compound generation

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 100
• Output Consistency: Variable
• Interpretation: Up to 100 molecules built around the inputs scaffold by MoLeR, steered by Enamine fragment latent codes.

Below are the Output Columns of the model:

10 of 100 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9taz⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9taz.zip⁠

⁠Resource Consumption

• Model Size (Mb): 23
• Environment Size (Mb): 2112
• Image Size (Mb): 2147.28

Computational Performance (seconds):

• 10 inputs: 50.56
• 100 inputs: 817.71
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/microsoft/molecule-generation⁠
• Publication: https://doi.org/10.48550/arXiv.2103.03864⁠
• Publication Type: Preprint
• Publication Year: 2022
• Ersilia Contributor: anamika-yadav99⁠

⁠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 eos9taz

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

# serve the model
ersilia serve eos9taz
# 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!