ersiliaos/eos8aa5 repository overview

⁠Knowledge-guided pre-trained graph transformer

Neural fingerprints (embeddings) based on a knowledge-guided graph transformer. This model reprsents a novel self-supervised learning framework for the representation learning of molecular graphs, consisting of a novel graph transformer architecture, LiGhT, and a knowledge-guided pre-training strategy.

This model was incorporated on 2024-12-17.Last packaged on 2026-08-31.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos8aa5
• Slug: kgpgt-embedding

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 2304
• Output Consistency: Fixed
• Interpretation: Knowledge-driven embedding

Below are the Output Columns of the model:

10 of 2304 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos8aa5⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos8aa5.zip⁠

⁠Resource Consumption

• Model Size (Mb): 344
• Environment Size (Mb): 1612
• Image Size (Mb): 1979.4

Computational Performance (seconds):

• 10 inputs: 29.44
• 100 inputs: 29.28
• 10000 inputs: 764.67

⁠References

• Source Code: https://github.com/lihan97/KPGT⁠
• Publication: https://doi.org/10.1038/s41467-023-43214-1⁠
• Publication Type: Peer reviewed
• Publication Year: 2023
• Ersilia Contributor: miquelduranfrigola⁠

⁠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 eos8aa5

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

# serve the model
ersilia serve eos8aa5
# 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!