Publish EnzymeCAGE pretrained checkpoints

LICENSE

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+Creative Commons Attribution-NonCommercial 4.0 International Public License
+By exercising the Licensed Rights, You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License ("Public License"). To the extent this Public License may be interpreted as a contract, You are granted the Licensed Rights in consideration of Your acceptance of these terms and conditions, and the Licensor grants You such rights in consideration of benefits the Licensor receives from making the Licensed Material available under these terms and conditions.
+
+
+
+Use Restrictions
+No Commercial Use.  You may only use the AI Model, Contributions, Derivative Works, Outputs and Output Derivatives (as defined below) for Non-Commercial Purposes. Any commercial use of any of the foregoing, including, without limitation, any use by, on behalf of or for any Commercial Entity or to facilitate the development of any product or service to be sold or made available for a fee, is strictly prohibited under this Agreement.
+
+No Use in Drug Development or Discovery. Without limiting the foregoing, You may not use the AI Model or any Contributions, Derivative Works, Outputs or Output Derivatives in or in connection with: (i) the development (at any stage) or discovery of any drug, medication or pharmaceutical of any kind; (ii) any molecular or biological target, hit or lead identification; (iii) drug candidate selection; or (iv) lead optimization.
+
+Use of Outputs.  Notwithstanding anything to the contrary in this Agreement, You may not use or provide access to any Outputs or Output Derivatives to train, optimize, improve or otherwise influence the functionality or performance of any: (i) other large language model; (ii) technology for protein structure prediction; or (iii) other Third Party Model that is similar to the AI Model. You may, however, use the Outputs and Outputs Derivatives to train, optimize, improve or otherwise influence the functionality or performance of the AI Model itself and downstream Derivative Works thereof.

README.md

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+---
+license: other
+license_name: enzymecage-research-license
+license_link: https://huggingface.co/GENTEL-Lab/EnzymeCAGE/blob/main/LICENSE
+tags:
+  - enzyme-retrieval
+  - geometric-deep-learning
+  - protein
+---
+
+# EnzymeCAGE pretrained checkpoints
+
+Pretrained epoch 19 checkpoints for [EnzymeCAGE](https://github.com/GENTEL-lab/EnzymeCAGE), *A Geometric Foundation Model for Enzyme Retrieval with Evolutionary Insights* (Nature Catalysis, 2026). These are PyTorch state dictionaries for the repository's model code, with seeds 40–44 under `pretrain/`. The repository's demo and evaluation configs use `pretrain/seed_42/epoch_19.pth`.
+
+## Download and run
+
+From the EnzymeCAGE repository root:
+
+```bash
+pip install -U huggingface_hub
+hf download GENTEL-Lab/EnzymeCAGE pretrain/seed_42/epoch_19.pth --local-dir checkpoints
+python infer.py --config config/demo/infer.yaml
+```
+
+To download all five seeds instead:
+
+```bash
+hf download GENTEL-Lab/EnzymeCAGE --include "pretrain/*/epoch_19.pth" --local-dir checkpoints
+```
+
+The dataset and generated features needed for inference are described in the [GitHub README](https://github.com/GENTEL-lab/EnzymeCAGE#dataset). A file list with sizes and SHA-256 checksums is in [`checkpoint_manifest.json`](checkpoint_manifest.json). The original `checkpoints.zip` is also linked from the GitHub README.
+
+## License
+
+See [LICENSE](LICENSE) for the full terms, including restrictions on commercial use and drug development or discovery.

checkpoint_manifest.json

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+{
+  "source_repository": "https://github.com/GENTEL-lab/EnzymeCAGE",
+  "source_checkpoint_directory": "checkpoints/pretrain",
+  "files": [
+    {
+      "path": "pretrain/seed_40/epoch_19.pth",
+      "size_bytes": 51337093,
+      "sha256": "80ff9be1f2f302f3ee444895687e0ab4187a6fad348df03a5c9814f06eb348ed"
+    },
+    {
+      "path": "pretrain/seed_41/epoch_19.pth",
+      "size_bytes": 51337093,
+      "sha256": "4e6bb2224317b5d3536763fa1cdb55d24980c9d773334ddf634a30b6a02ebeba"
+    },
+    {
+      "path": "pretrain/seed_42/epoch_19.pth",
+      "size_bytes": 51337093,
+      "sha256": "8ab2bb4bdbde2adff9949bf645daf4292cabe4095273b1022aadc7c292ef24c2"
+    },
+    {
+      "path": "pretrain/seed_43/epoch_19.pth",
+      "size_bytes": 51337093,
+      "sha256": "43a3523dfdef6d7ce4114035f93804f58ebff92777d9e190783a18f476187b2a"
+    },
+    {
+      "path": "pretrain/seed_44/epoch_19.pth",
+      "size_bytes": 51337093,
+      "sha256": "d118695430094c82655ca1b1991c93487b4cf38e92c1e28da272accb5de0630a"
+    }
+  ]
+}

pretrain/seed_40/epoch_19.pth

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+size 51337093

pretrain/seed_41/epoch_19.pth

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pretrain/seed_42/epoch_19.pth

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pretrain/seed_43/epoch_19.pth

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+size 51337093

pretrain/seed_44/epoch_19.pth

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