File size: 1,515 Bytes
fcb9799 b8b8763 759172e fcb9799 44b1a31 02cc741 fcb9799 ef38f95 fcb9799 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 |
---
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`
|