ersiliaos/eos4avb repository overview

⁠Molecular representation learning

Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks.

This model was incorporated on 2023-01-25.Last packaged on 2026-08-31.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos4avb
• Slug: image-mol-embeddings

⁠Domain

• Task: Representation
• Subtask: Featurization
• Biomedical Area: Any
• Target Organism: Any
• Tags: Embedding

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 512
• Output Consistency: Fixed
• Interpretation: ImageMol embeddings of shape [512] reshaped as a Numpy 1D array before serializing. These embeddings can be used as the input features of a fully connected classification or regression layer in a neural network.

Below are the Output Columns of the model:

10 of 512 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos4avb⁠
• Docker Architecture: AMD64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos4avb.zip⁠

⁠Resource Consumption

• Model Size (Mb): 66
• Environment Size (Mb): 1194
• Image Size (Mb): 1432.26

Computational Performance (seconds):

• 10 inputs: 24.89
• 100 inputs: 17.2
• 10000 inputs: 193.85

⁠References

• Source Code: https://github.com/HongxinXiang/ImageMol⁠
• Publication: https://doi.org/10.1038/s42256-022-00557-6⁠
• Publication Type: Peer reviewed
• Publication Year: 2022
• Ersilia Contributor: DhanshreeA⁠

⁠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 eos4avb

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

# serve the model
ersilia serve eos4avb
# 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!