
## 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`

molecules chemistry SMILES region:us
{"tags": ["molecules", "chemistry", "SMILES"]}
TAGS
#molecules #chemistry #SMILES #region-us 


## 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



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] URL

### Pre-process yourself

To manually perform the preprocessing, download the data sets from

1. BindingDB

In 'bindingdb', download the database as tab separated values
<URL> > Download > BindingDB_All_2021m4.URL
and extract the zip archive into 'bindingdb/data'

Run the steps in 'URL'

2. PDBBind-cn

Register for an account at <URL 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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

3. BindingMOAD

Go to <URL> and download the files 'URL'
(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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

4. BioLIP

Download from <URL the files
- URL.bz2 (Receptor1, Non-redudant set)
- ligand_2013-URL.bz2 (Ligands)
- URL.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-URL'

Run the script 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

5. Final concatenation and filtering

Run the steps in the notebook 'combine_dbs.ipynb'

## 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



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] URL ### Pre-process yourself

To manually perform the preprocessing, download the data sets from

1. BindingDB

In 'bindingdb', download the database as tab separated values
<URL> > Download > BindingDB_All_2021m4.URL
and extract the zip archive into 'bindingdb/data'

Run the steps in 'URL'

2. PDBBind-cn

Register for an account at <URL 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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

3. BindingMOAD

Go to <URL> and download the files 'URL'
(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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

4. BioLIP

Download from <URL the files
- URL.bz2 (Receptor1, Non-redudant set)
- ligand_2013-URL.bz2 (Ligands)
- URL.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-URL'

Run the script 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

5. Final concatenation and filtering

Run the steps in the notebook 'combine_dbs.ipynb'
TAGS
#molecules #chemistry #SMILES #region-us 
 ## 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



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] URL ### Pre-process yourself

To manually perform the preprocessing, download the data sets from

1. BindingDB

In 'bindingdb', download the database as tab separated values
<URL> > Download > BindingDB_All_2021m4.URL
and extract the zip archive into 'bindingdb/data'

Run the steps in 'URL'

2. PDBBind-cn

Register for an account at <URL 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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

3. BindingMOAD

Go to <URL> and download the files 'URL'
(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 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

4. BioLIP

Download from <URL the files
- URL.bz2 (Receptor1, Non-redudant set)
- ligand_2013-URL.bz2 (Ligands)
- URL.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-URL'

Run the script 'URL' in a compute job on an MPI-enabled cluster
(e.g., 'mpirun -n 64 URL').

Perform the steps in the notebook 'URL'

5. Final concatenation and filtering

Run the steps in the notebook 'combine_dbs.ipynb'