| --- |
| tags: |
| - molecules |
| - chemistry |
| - SMILES |
| --- |
| |
| ## How to use the data sets |
|
|
| This dataset contains 1.9M unique pairs of protein sequences and ligand SMILES with experimentally determined |
| binding affinities. It can be used for fine-tuning a language model. |
|
|
| The data comes from the following sources: |
| - BindingDB |
| - PDBbind-cn |
| - BioLIP |
| - BindingMOAD |
|
|
| ### 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%:]') |
| ``` |
|
|
| Optionally, datasets with certain protein sequences removed are available. |
| These can be used to test the predictive power for specific proteins even when |
| these are not part of the training data. |
|
|
| - `train_no_kras` (no KRAS proteins) |
|
|
| **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` |
|
|
