ersiliaos/eos8fma repository overview

⁠STONED analogue generation by SELFIES mutation

Generates up to 100 analogues of the input by making one or two random mutations to its SELFIES string, exploiting the fact that any such string decodes to a valid molecule. STONED, from Nigam and colleagues, showed that this untrained procedure matches far heavier generative models on standard benchmarks and also builds chemical paths between two molecules. Ersilia draws 5000 raw candidates per input, keeps only those whose Morgan Tanimoto similarity to the seed lies between 0.6 and 0.9, picks up to 100 of them at random and lists them most similar first. Sampling is unseeded.

This model was incorporated on 2023-08-08.Last packaged on 2026-10-26.

⁠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 analogues from unseeded SELFIES mutation, picked at random among those at Morgan Tanimoto similarity 0.6 to 0.9, listed most similar 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/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): 867
• Image Size (Mb): 878.46

Computational Performance (seconds):

• 10 inputs: 34.07
• 100 inputs: 598.64
• 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!