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--- |
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tags: |
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- molecules |
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- chemistry |
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- SMILES |
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--- |
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## How to use the data sets |
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This dataset contains more than 16,000 unique pairs of protein sequences and ligand SMILES with experimentally determined |
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binding affinities and protein-ligand contacts (ligand atom/SMILES token vs. Calpha within 5 Angstrom). These |
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are represented by a list that contains the positions of non-zero elements of the flattened, sparse |
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sequence x smiles tokens (2048x512) matrix. The first and last entries in both dimensions |
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are padded to zero, they correspond to [CLS] and [SEP]. |
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It can be used for fine-tuning a language model. |
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The data solely uses data from PDBind-cn. |
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Contacts are calculated at four cut-off distances: 5, 8, 11A and 15A. |
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### Use the already preprocessed data |
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Load a test/train split using |
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``` |
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from datasets import load_dataset |
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train = load_dataset("jglaser/protein_ligand_contacts",split='train[:90%]') |
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validation = load_dataset("jglaser/protein_ligand_contacts",split='train[90%:]') |
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``` |
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### Pre-process yourself |
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To manually perform the preprocessing, download the data sets from P.DBBind-cn |
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Register for an account at <https://www.pdbbind.org.cn/>, confirm the validation |
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email, then login and download |
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- the Index files (1) |
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- the general protein-ligand complexes (2) |
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- the refined protein-ligand complexes (3) |
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Extract those files in `pdbbind/data` |
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Run the script `pdbbind.py` in a compute job on an MPI-enabled cluster |
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(e.g., `mpirun -n 64 pdbbind.py`). |
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Perform the steps in the notebook `pdbbind.ipynb` |
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