| | --- |
| | 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` |
| |
|
