ersiliaos/eos633t repository overview

⁠Scaffold-based generation with MoLeR

MoLeR, a graph-based variational autoencoder from Microsoft Research and Novartis, builds molecules motif by motif from a starting scaffold and was trained on GuacaMols about 1.5 million drug-like molecules. Ersilia keeps only the Murcko scaffold of the input, discarding its side chains, and decodes onto it the latent codes of random molecules from Enamines 254k building-block library; the blocks steer the decoder and are not attached. Up to 100 distinct molecules come back, nearly all containing the scaffold and often adding rings. Acyclic inputs return nothing. Unseeded, so runs differ.

This model was incorporated on 2023-11-03.Last packaged on 2026-10-26.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos633t
• Slug: moler-enamine-blocks

⁠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 building-block latent codes, most similar to the input first.

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/eos633t⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos633t.zip⁠

⁠Resource Consumption

• Model Size (Mb): 33
• Environment Size (Mb): 2094
• Image Size (Mb): 2188.39

Computational Performance (seconds):

• 10 inputs: 34.35
• 100 inputs: 1001.34
• 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: 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 eos633t

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

# serve the model
ersilia serve eos633t
# 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!