metadata.json

{
    "Identifier": "eos4avb",
    "Slug": "image-mol-embeddings",
    "Status": "Ready",
    "Title": "Molecular representation learning",
    "Description": "Representation Learning Framework that utilizes molecule images for encoding molecular inputs as machine readable vectors for downstream tasks such as bio-activity prediction, drug metabolism analysis, or drug toxicity prediction. The approach utilizes transfer learning, that is, pre-training the model on massive unlabeled datasets to help it in generalizing feature extraction and then fine tuning on specific tasks.",
    "Mode": "Pretrained",
    "Task": [
        "Representation"
    ],
    "Input": [
        "Compound"
    ],
    "Input Shape": "Single",
    "Output": [
        "Descriptor"
    ],
    "Output Type": [
        "Float"
    ],
    "Output Shape": "Matrix",
    "Interpretation": "ImageMol embeddings of shape [1512] reshaped as a Numpy 1D array before serializing. These embeddings can be used as the input features of a fully connected classification or regression layer in a neural network.",
    "Tag": [
        "Embedding"
    ],
    "Publication": "https://www.nature.com/articles/s42256-022-00557-6",
    "Source Code": "https://github.com/HongxinXiang/ImageMol",
    "License": "MIT",
    "S3": "https://ersilia-models-zipped.s3.eu-central-1.amazonaws.com/eos4avb.zip",
    "DockerHub": "https://hub.docker.com/r/ersiliaos/eos4avb",
    "Docker Architecture": [
        "AMD64"
    ]
}