tags:
- molecules
- chemistry
- SMILES
How to use the data sets
This dataset contains more than 16,000 unique pairs of protein sequences and ligand SMILES with experimentally determined binding affinities and protein-ligand contacts (ligand atom/SMILES token vs. Calpha within 5 Angstrom). These are represented by a list that contains the positions of non-zero elements of the flattened, sparse sequence x smiles tokens (2048x512) matrix. The first and last entries in both dimensions are padded to zero, they correspond to [CLS] and [SEP].
It can be used for fine-tuning a language model.
The data solely uses data from PDBind-cn.
Contacts are calculated at four cut-off distances: 5, 8, 11A and 15A.
Use the already preprocessed data
Load a test/train split using
from datasets import load_dataset
train = load_dataset("jglaser/protein_ligand_contacts",split='train[:90%]')
validation = load_dataset("jglaser/protein_ligand_contacts",split='train[90%:]')
Pre-process yourself
To manually perform the preprocessing, download the data sets from P.DBBind-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