ersiliaos/eos8fma repository overview

⁠STONED analogue generation by SELFIES mutation

The STONED sampler uses small modifications to molecules represented as SELFIES to perform a search of the chemical space and generate new molecules. The use of string modifications in the SELFIES molecular representation bypasses the need for large amounts of data while maintaining a performance comparable to deep generative models.

This model was incorporated on 2023-08-08.Last packaged on 2026-09-29.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos8fma
• Slug: stoned-sampler

⁠Domain

• Task: Sampling
• Subtask: Generation
• Biomedical Area: Any
• Target Organism: Any
• Tags: Compound generation

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 100
• Output Consistency: Variable
• Interpretation: Up to 100 derivatives of the input, most to least similar. Only candidates with a Morgan Tanimoto similarity to the input between 0.6 and 0.9 are kept, so fewer than 100 may be returned for some inputs; unused columns are empty.

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/eos8fma⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos8fma.zip⁠

⁠Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 873
• Image Size (Mb): 876.34

Computational Performance (seconds):

• 10 inputs: 31.21
• 100 inputs: 428.16
• 10000 inputs: -1

⁠References

• Source Code: https://github.com/aspuru-guzik-group/stoned-selfies⁠
• Publication: https://doi.org/10.1039/d1sc00231g⁠
• Publication Type: Peer reviewed
• Publication Year: 2021
• Ersilia Contributor: GemmaTuron⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a Apache-2.0⁠ 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 eos8fma

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

# serve the model
ersilia serve eos8fma
# 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!