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---
dataset_info:
features:
- name: seq
dtype: string
- name: smiles
dtype: string
- name: affinity_uM
dtype: float64
- name: neg_log10_affinity_M
dtype: float64
- name: smiles_can
dtype: string
- name: affinity
dtype: float64
- name: selfies
dtype: string
splits:
- name: train
num_bytes: 872969895.9256648
num_examples: 1082521
download_size: 110659389
dataset_size: 872969895.9256648
---
# From the [jglaser/binding_affinity](https://huggingface.co/datasets/jglaser/binding_affinity) database, converted to SELFIES
Steps to prepare the database:
1. Download the jglaser/binding_affinity database
```python
from datasets import load_dataset
binding_data = load_dataset('parquet', split='train', data_files="https://huggingface.co/datasets/jglaser/binding_affinity/resolve/main/data/all_512.parquet")
```
2. Convert SMILES to [SELFIES](https://github.com/aspuru-guzik-group/selfies)
```python
import selfies
def smiles_to_selfies(dataset):
try:
return {"selfies": selfies.encoder(dataset["smiles_can"])}
except selfies.EncoderError:
return {"selfies": None}
binding_data = binding_data.map(smiles_to_selfies)
binding_data = binding_data.filter(lambda dataset: dataset["selfies"] != None)
```
3. Compute protein ('prot_bert') / ligand embeddings ('SELFIES-RoBERTa-PubChem10M')
```python
from sentence_transformers import SentenceTransformer
import re
import pickle
import os.path
# Protein embeddings
all_proteins_shortened = binding_data.unique('seq')
protein_sequences = [re.sub(r"[UZOB]", "X", sequence) for sequence in all_proteins_shortened]
protein_sequences = [" ".join(sequence) for sequence in protein_sequences]
protein_model = SentenceTransformer('Rostlab/prot_bert')
protein_model.max_seq_length = 512
protein_emb = protein_model.encode(protein_sequences)
protein_embeddings = dict(zip(protein_sequences, protein_emb))
with open('protein_embeddings.pkl', "wb") as fOut:
pickle.dump(protein_embeddings, fOut, protocol=pickle.HIGHEST_PROTOCOL)
# Ligand embeddings
all_selfies_shortened = binding_data.unique('selfies')
ligand_sequences = [sequence for sequence in all_selfies_shortened]
ligand_model = SentenceTransformer('ejy/SELFIES-RoBERTa-PubChem10M')
ligand_model.max_seq_length = 128
ligand_emb = ligand_model.encode(ligand_sequences)
ligand_embeddings = dict(zip(ligand_sequences, ligand_emb))
with open('ligand_embeddings.pkl', "wb") as fOut:
pickle.dump(ligand_embeddings, fOut, protocol=pickle.HIGHEST_PROTOCOL)
``` |