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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 1.9M unique pairs of protein sequences and ligand SMILES with experimentally determined |
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binding affinities. It can be used for fine-tuning a language model. |
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The data comes from the following sources: |
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- BindingDB |
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- PDBbind-cn |
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- BioLIP |
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- BindingMOAD |
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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/binding_affinity",split='train[:90%]') |
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validation = load_dataset("jglaser/binding_affinity",split='train[90%:]') |
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``` |
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Optionally, datasets with certain protein sequences removed are available. |
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These can be used to test the predictive power for specific proteins even when |
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these are not part of the training data. |
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- `train_no_kras` (no KRAS proteins) |
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**Loading the data manually** |
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The file `data/all.parquet` contains the preprocessed data. To extract it, |
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you need download and install [git LFS support] https://git-lfs.github.com/]. |
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### Pre-process yourself |
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To manually perform the preprocessing, download the data sets from |
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1. BindingDB |
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In `bindingdb`, download the database as tab separated values |
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<https://bindingdb.org> > Download > BindingDB_All_2021m4.tsv.zip |
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and extract the zip archive into `bindingdb/data` |
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Run the steps in `bindingdb.ipynb` |
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2. PDBBind-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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3. BindingMOAD |
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Go to <https://bindingmoad.org> and download the files `every.csv` |
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(All of Binding MOAD, Binding Data) and the non-redundant biounits |
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(`nr_bind.zip`). Place and extract those files into `binding_moad`. |
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Run the script `moad.py` in a compute job on an MPI-enabled cluster |
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(e.g., `mpirun -n 64 moad.py`). |
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Perform the steps in the notebook `moad.ipynb` |
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4. BioLIP |
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Download from <https://zhanglab.ccmb.med.umich.edu/BioLiP/> the files |
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- receptor1.tar.bz2 (Receptor1, Non-redudant set) |
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- ligand_2013-03-6.tar.bz2 (Ligands) |
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- BioLiP.tar.bz2 (Annotations) |
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and extract them in `biolip/data`. |
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The following steps are **optional**, they **do not** result in additional binding affinity data. |
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Download the script |
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- download_all_sets.pl |
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from the Weekly update subpage. |
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Update the 2013 database to its current state |
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`perl download_all-sets.pl` |
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Run the script `biolip.py` in a compute job on an MPI-enabled cluster |
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(e.g., `mpirun -n 64 biolip.py`). |
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Perform the steps in the notebook `biolip.ipynb` |
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5. Final concatenation and filtering |
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Run the steps in the notebook `combine_dbs.ipynb` |
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