ersiliaos/eos8aa5 repository overview

⁠Knowledge-guided pre-trained graph transformer

Encodes a molecule into 2,304 features with KPGT, whose backbone treats the molecular line graph so that bonds become nodes and a transformer can attend across the whole structure. Li and colleagues pretrained it on about two million ChEMBL compounds using a masked-graph objective augmented with a knowledge node carrying molecular descriptors and fingerprints, so established chemistry guides the representation instead of the network rediscovering it. The embedding transferred across 63 property prediction datasets, and its individual dimensions carry no separate meaning.

This model was incorporated on 2024-12-17.Last packaged on 2026-10-07.

⁠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: 2304 features encoding molecular structure from a knowledge-guided pretrained graph transformer.

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): 1611
• Image Size (Mb): 1982.44

Computational Performance (seconds):

• 10 inputs: 28.5
• 100 inputs: 28.79
• 10000 inputs: 784.25

⁠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!