ersiliaos/eos9zw0 repository overview

⁠Molecular Prediction Model Fine-Tuning (MolPMoFiT) encodings

Represents a molecule as 1,200 features drawn from MolPMoFiT, an AWD-LSTM language model that Li and Fourches pretrained on one million unlabelled ChEMBL structures by adapting the ULMFiT inductive transfer learning recipe from natural language, then fine-tuned for lipophilicity, solvation, HIV activity and blood-brain barrier penetration. Only the pretrained encoder is served, not any fine-tuned endpoint; its last LSTM layer is summarised by ULMFiT concat pooling (last hidden state, max-pool and mean-pool, 400 features each), the input its prediction heads use.

This model was incorporated on 2023-11-06.Last packaged on 2026-10-07.

⁠Information

⁠Identifiers

• Ersilia Identifier: eos9zw0
• Slug: molpmofit

⁠Domain

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

⁠Input

• Input: Compound
• Input Dimension: 1

⁠Output

• Output Dimension: 1200
• Output Consistency: Fixed
• Interpretation: Last hidden state, max-pool and mean-pool of the final LSTM layer of a ChEMBL-pretrained SMILES language model, 400 features each.

Below are the Output Columns of the model:

10 of 1200 columns are shown

⁠Source and Deployment

• Source: Local
• Source Type: External
• DockerHub: https://hub.docker.com/r/ersiliaos/eos9zw0⁠
• Docker Architecture: AMD64, ARM64
• S3 Storage: https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9zw0.zip⁠

⁠Resource Consumption

• Model Size (Mb): 122
• Environment Size (Mb): 7262
• Image Size (Mb): 7501.1

Computational Performance (seconds):

• 10 inputs: 29.68
• 100 inputs: 23.32
• 10000 inputs: 296.57

⁠References

• Source Code: https://github.com/XinhaoLi74/MolPMoFiT⁠
• Publication: https://doi.org/10.1186/s13321-020-00430-x⁠
• Publication Type: Peer reviewed
• Publication Year: 2020
• Ersilia Contributor: GemmaTuron⁠

⁠License

This package is licensed under a GPL-3.0⁠ license. The model contained within this package is licensed under a None⁠ 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 eos9zw0

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

# serve the model
ersilia serve eos9zw0
# 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!