binding_affinity / README.md
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## How to use the data sets
### Use the already preprocessed data
The file `data/all.parquet` contains the preprocessed data. Load the dataset using
```
from datasets import load_dataset
dataset = load_dataset("jglaser/binding_affinity")
```
### Pre-process yourself
To manually perform the preprocessing, fownload the data sets from
1. BindingDB
In `bindingdb`, download the database as tab separated values
[https://bindingdb.org] > Download > BindingDB_All_2021m4.tsv.zip
and extract the zip archive into `bindingdb/data`
Run the steps in `bindingdb.ipynb`
2. PDBBind-cn
Register for an account at [https://www.pdbbind.org.cn/], confirm the validation
email, then login and download
- the Index files (1)
- the general protein-ligand complexes (2)
- the refined protein-ligand complexes (3)
Extract those files in `pdbbind/data`
Run the script `pdbbind.py` in a compute job on an MPI-enabled cluster
(e.g., `mpirun -n 64 pdbbind.py`).
Perform the steps in the notebook `pdbbind.ipynb`
3. BindingMOAD
Go to [https://bindingmoad.org] and download the files `every.csv`
(All of Binding MOAD, Binding Data) and the non-redundant biounits
(`nr_bind.zip`). Place and extract those files into `binding_moad`.
Run the script `moad.py` in a compute job on an MPI-enabled cluster
(e.g., `mpirun -n 64 moad.py).
Perform the steps in the notebook `moad.ipynb`
4. BioLIP
Download from [https://zhanglab.ccmb.med.umich.edu/BioLiP/] the files
- receptor_nr1.tar.bz2 (Receptor1, Non-redudant set)
- ligand_nr.tar.bz2 (Ligands)
- BioLiP_nr.tar.bz2 (Annotations)
and extract them in `biolip/data`.
Run the script `biolip.py` in a compute job on an MPI-enabled cluster
(e.g., `mpirun -n 64 biolip.py).
Perform sthe steps in the notebook `biolip.ipynb`
5. Final concatenation and filtering
Run the steps in the notebook `combine_dbs.ipynb`