metadata.json

{
    "Identifier": "eos7w6n",
    "Slug": "grover-embedding",
    "Status": "Ready",
    "Title": "Large-scale graph transformer",
    "Description": "GROVER is a self-supervised Graph Neural Network for molecular representation pretrained with 10 million unlabelled molecules from ChEMBL and ZINC15. The model provided has been pre-trained on 10 million molecules (GROVERlarge). GROVER has then been fine-tuned to predict several activities from the MoleculeNet benchmark, consistently outperforming other state-of-the-art methods for serveral benchmark datasets.\n",
    "Mode": "Pretrained",
    "Input": [
        "Compound"
    ],
    "Input Shape": "Single",
    "Task": [
        "Representation"
    ],
    "Output": [
        "Descriptor"
    ],
    "Output Type": [
        "Float"
    ],
    "Output Shape": "List",
    "Interpretation": "Embedding representation of a molecule",
    "Tag": [
        "Chemical graph model",
        "Embedding",
        "Descriptor"
    ],
    "Publication": "https://papers.nips.cc/paper/2020/file/94aef38441efa3380a3bed3faf1f9d5d-Paper.pdf",
    "Source Code": "https://github.com/tencent-ailab/grover",
    "License": "MIT",
    "Contributor": "miquelduranfrigola",
    "S3": "https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos7w6n.zip",
    "DockerHub": "https://hub.docker.com/r/ersiliaos/eos7w6n",
    "Docker Architecture": [
        "AMD64",
        "ARM64"
    ]
}