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.
- EOS model ID:
eos4avb - Slug:
image-mol-embeddings
- Input:
Compound - Input Shape:
Single - Task:
Representation - 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.
- Publication
- Source Code
- Ersilia contributor: DhanshreeA
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