Molecular maps based on broadly learned knowledge-based representations

Molecular representation of small molecules via descriptor-based molecular maps (images). The fingerprint-based molecular maps are available at eos59rr. These images can be used as inputs for an image-based deep learning model such as a convolutional neural network. The authors have demonstrated high performance of MolMap out-of-the-box with a broad range of tasks from MoleculeNet.

Identifiers

• EOS model ID: eos6m4j
• Slug: bidd-molmap-desc

Characteristics

• Input: Compound
• Input Shape: Single
• Task: Generative
• Output: Image, Descriptor
• Output Type: Float
• Output Shape: Matrix
• Interpretation: Image representation of a molecule. Each pixel represents a molecular feature

References

• Publication
• Source Code
• Ersilia contributor: miquelduranfrigola

Ersilia model URLs

• GitHub
• AWS S3
• DockerHub (AMD64)

Citation

If you use this model, please cite the original authors of the model and the Ersilia Model Hub.

License

This package is licensed under a GPL-3.0 license. The model contained within this package is licensed under a GPL-3.0 license.

Notice: Ersilia grants access to these models 'as is' provided by the original authors, please refer to the original code repository and/or publication if you use the model in your research.

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