ersiliaos/eos2mrz

DeepSMILES, an alternate SMILES representation for deep learning

DeepSMILES converts a SMILES string to a more accurate syntax for molecule representation, taking into account both the branches (closed parenthesis in the SMILES strings) and rings (using a single symbol at ring closure that also indicates ring size). This syntax is particularly suitable in generative models, when the output is a SMILES string. With DeepSMILES, scientists can train a network using this new syntax, generate new molecules represented as DeepSMILES and then decode them back to normal SMILES strings.

This model was incorporated on 2022-07-21.Last packaged on 2025-10-14.

Information

Identifiers

• Ersilia Identifier: eos2mrz
• Slug: deepsmiles

Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Chemical language model, Chemical notation

Input

• Input: Compound
• Input Dimension: 1

Output

• Output Dimension: 1
• Output Consistency: Fixed
• Interpretation: String representing a DeepSMILES

Below are the Output Columns of the model:

Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos2mrz
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos2mrz.zip

Resource Consumption

• Model Size (Mb): 1
• Environment Size (Mb): 317
• Image Size (Mb): 295.47

Computational Performance (seconds):

• 10 inputs: 27.25
• 100 inputs: 16.9
• *** inputs: 19.32

References

• Source Code: https://github.com/baoilleach/deepsmiles
• Publication: [***]
• Publication Type: Preprint
• Publication Year: 2018
• Ersilia Contributor: https://github.com/brosular

License

This package is licensed under a https://github.com/ersilia-os/ersilia/blob/master/LICENSE 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 https://github.com/ersilia-os/ersilia installed. The model can be fetched using the following command:

bash
# fetch model from the Ersilia Model Hub
ersilia fetch eos2mrz

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

bash
# serve the model
ersilia serve eos2mrz
# 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 https://ersilia.io is a tech non-profit organization fueling sustainable research in the Global South. Please https://github.com/ersilia-os/ersilia/blob/master/CITATION.cff the Ersilia Model Hub if you've found this model to be useful. Always https://github.com/ersilia-os/ersilia/issues if you experience any issues while trying to run it. If you want to contribute to our mission, *** https://www.ersilia.io/donate to Ersilia!