ersiliaos/eos9taz repository overview

⁠Extending molecular scaffolds with fragments

MoLeR is a graph-based generative model that combines fragment-based and atom-by-atom generation of new molecules with scaffold-constrained optimization. It does not depend on generation history and therefore MoLeR is able to complete arbitrary scaffolds. The model has been trained on the GuacaMol dataset. Here we sample a fragment library from Enamine.

This model was incorporated on 2022-12-06.Last packaged on 2026-09-21.

⁠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: 1000
• Output Consistency: Fixed
• Interpretation: Up to 1000 unique molecules generated per input, preserving its scaffold; some scaffolds yield fewer unique molecules than requested.

Below are the Output Columns of the model:

10 of 1000 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): 2111
• Image Size (Mb): 2160.53

Computational Performance (seconds):

• 10 inputs: 96.36
• 100 inputs: -1
• 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!