metadata.json

{
    "Identifier": "eos9zw0",
    "Slug": "molpmofit",
    "Status": "Ready",
    "Title": "Molecular Prediction Model Fine-Tuning (MolPMoFiT)",
    "Description": "Using self-supervised learning, the authors pre-trained a large model using one millon unlabelled molecules from ChEMBL. This model can subsequently be fine-tuned for various QSAR tasks. Here, we provide the encodings for the molecular structures using the pre-trained model, not the fine-tuned QSAR models.",
    "Mode": "Pretrained",
    "Task": [
        "Representation"
    ],
    "Input": [
        "Compound"
    ],
    "Input Shape": "Single",
    "Output": [
        "Other value"
    ],
    "Output Type": [
        "Float"
    ],
    "Output Shape": "Matrix",
    "Interpretation": "Embedding vectors of each smiles are obtained, represented in a matrix, where each row is a vector of embedding of each smiles character, with a dimension of 400. The pretrained model is loaded using the fastai library",
    "Tag": [
        "Descriptor",
        "Embedding"
    ],
    "Publication": "https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00430-x",
    "Source Code": "https://github.com/XinhaoLi74/MolPMoFiT",
    "License": "CC",
    "S3": "https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos9zw0.zip",
    "DockerHub": "https://hub.docker.com/r/ersiliaos/eos9zw0",
    "Docker Architecture": [
        "AMD64",
        "ARM64"
    ]
}