| ## 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 | |
| - 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` | |