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license: cc-by-4.0 |
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## 1. Files |
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### gnn_cnn_data.zip |
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The dataset used to train gnn and cnn-3d model. |
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### split_60.zip |
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The dataset used to train Uni-Mol or ProFSA model for the SIU 0.6 version. It contains train.lmdb, valid.lmdb and test.lmdb. |
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### split_90.zip |
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The dataset used to train Uni-Mol or ProFSA model for the SIU 0.9 version. It contains train.lmdb, valid.lmdb and test.lmdb. |
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### pretrain_weights.zip |
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Pretrained weights for Uni-Mol and ProFSA. |
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profsa.pt: weights for pretrained ProFSA model |
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mol_pre_no_h_220816.pt: weights for pretrained Uni-Mol molecular Encoder |
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pocket_pre_220816.pt: weights for pretrained Uni-Mol pocketr Encoder |
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### final_dic.pkl |
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Complete dataset file in pickle format |
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### result_structures.zip |
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contains all structure files for protein and docked small molecules. |
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## 2. Dataset Format |
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### final_dic.pkl |
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each key is an uniprot id |
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corresponding value is a list of dictionaries. Each dictionary is a data point and has following keys: |
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| Key | Description | |
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|----------------|----------------| |
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| atoms | atom types in ligand | |
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| coordinates | list of different conformations of the ligand | |
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| pocket_atoms| atom types in pocket | |
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| pocket_coordinates | atom positions of the pocket | |
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| source_data | pdbid and uniprot id information | |
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| label | dictionary for different labels | |
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| ik | inchikey of ligand | |
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| smi | smiles of ligand | |
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### Other lmdb files |
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All data are in lmdb format, and have the same keys as shown above. |
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Note that for single task learning, the albel is a float value instead of a dictionary. |
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