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## How to use the data sets
### Use the already preprocessed data
Load a test/train split using
```
from datasets import load_dataset
train = load_dataset("jglaser/binding_affinity",split='train[:90%]')
validation = load_dataset("jglaser/binding_affinity",split='train[90%:]')
```
**Loading the data manually**
The file `data/all.parquet` contains the preprocessed data. To extract it,
you need download and install [git LFS support] https://git-lfs.github.com/].
### Pre-process yourself
To manually perform the preprocessing, download 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
- receptor1.tar.bz2 (Receptor1, Non-redudant set)
- ligand_2013-03-6.tar.bz2 (Ligands)
- BioLiP.tar.bz2 (Annotations)
and extract them in `biolip/data`.
The following steps are **optional**, they **do not** result in additional binding affinity data.
Download the script
- download_all_sets.pl
from the Weekly update subpage.
Update the 2013 database to its current state
`perl download_all-sets.pl`
Run the script `biolip.py` in a compute job on an MPI-enabled cluster
(e.g., `mpirun -n 64 biolip.py`).
Perform the steps in the notebook `biolip.ipynb`
5. Final concatenation and filtering
Run the steps in the notebook `combine_dbs.ipynb`
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