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update readme
Browse files- README.md +9 -12
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- assets/images/homology_detection.png +0 -0
- assets/images/reconstruction.png +0 -0
- assets/images/structure_prediction.png +0 -0
- assets/images/structure_tokenizer/launch_protein_viewer.png +0 -0
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- assets/images/structure_tokenizer/visualize_reconstruction.png +0 -0
README.md
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@@ -14,12 +14,12 @@ AIDO.StructureTokenizer is a VQ-VAE-based tokenizer designed for protein structu
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## Model Description
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**AIDO.StructureTokenizer** is built on a Vector Quantized Variational Autoencoder (VQ-VAE) architecture with the following components:
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- Equivariant Encoder: Encodes backbone structures into a latent space that maintains rotational and translational symmetries using the Equiformer architecture.
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- Discrete Codebook: Maps continuous latent vectors into 512 discrete structural tokens.
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- Invariant Decoder: Reconstructs full 3D structures, including side chains, from the structural tokens using an architecture adapted from ESMFold.
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This model strikes a balance between reconstruction fidelity and structural locality, optimizing its suitability for downstream tasks such as structure prediction, homology detection, and multimodal protein modeling.
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- Reconstructing Structures (See [below](#reconstructing-structures))
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- Structure Prediction (See [this section](https://huggingface.co/genbio-ai/AIDO.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/AIDO.Protein2StructureToken-16B)
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### Hyperparameters
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TODO
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### Training details
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TODO
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## Results
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## How to Use
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Please see `experiments/AIDO.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
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## Model Description
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![Model Architecture](./assets/images/architecture.png)
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**AIDO.StructureTokenizer** is built on a Vector Quantized Variational Autoencoder (VQ-VAE) architecture with the following components:
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- Equivariant Encoder (6M): Encodes backbone structures into a latent space that maintains rotational and translational symmetries using the Equiformer architecture.
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- Discrete Codebook: Maps continuous latent vectors into 512 discrete structural tokens.
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- Invariant Decoder (300M): Reconstructs full 3D structures, including side chains, from the structural tokens using an architecture adapted from ESMFold.
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This model strikes a balance between reconstruction fidelity and structural locality, optimizing its suitability for downstream tasks such as structure prediction, homology detection, and multimodal protein modeling.
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- Reconstructing Structures (See [below](#reconstructing-structures))
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- Structure Prediction (See [this section](https://huggingface.co/genbio-ai/AIDO.Protein2StructureToken-16B/blob/main/README.md#structure-prediction) in genbio-ai/AIDO.Protein2StructureToken-16B)
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## Results
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### Reconstructing Structures
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![Reconstruction Results](./assets/images/reconstruction.png)
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### Homology Detection
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![Homology Detection Results](./assets/images/homology_detection.png)
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### Structure Prediction
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![Structure Prediction Results](./assets/images/structure_prediction.png)
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## How to Use
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Please see `experiments/AIDO.StructureTokenizer` in [Model Generator](https://github.com/genbio-ai/modelgenerator) for more details.
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assets/images/architecture.png
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assets/images/homology_detection.png
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assets/images/reconstruction.png
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assets/images/structure_prediction.png
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assets/images/structure_tokenizer/launch_protein_viewer.png
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assets/images/structure_tokenizer/select_files.png
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assets/images/structure_tokenizer/visualize_reconstruction.png
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