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---
dataset_info:
features:
- name: Y_affinity
dtype: float64
- name: smiles
dtype: string
- name: seq
dtype: string
- name: Y_binary
dtype: int64
- name: Y_log_affinity
dtype: float64
- name: selfies
dtype: string
splits:
- name: train
num_bytes: 606248442.8293403
num_examples: 740566
download_size: 161743110
dataset_size: 606248442.8293403
---
# From the [GLASS GPCR database](https://aideepmed.com/GLASS1/), converted to SELFIES
Steps to prepare the database:
1. Download the GLASS database
```bash
wget https://zhanggroup.org/GLASS/downloads/interactions_active.tsv
wget https://zhanggroup.org/GLASS/downloads/interactions_inactives.tsv
wget https://zhanggroup.org/GLASS/downloads/targets.tsv
wget https://zhanggroup.org/GLASS/downloads/ligands.tsv
```
2. Select just the columns of interest
```bash
cut -d$'\t' -f6,9 ligands.tsv > ligands2.tsv
cut -d$'\t' -f2,5 targets.tsv > targets2.tsv
cut -d$'\t' -f1,2,4,5 interactions_active.tsv > interactions_active2.tsv
cut -d$'\t' -f1,2,4,5 interactions_inactives.tsv > interactions_inactives2.tsv
```
3. Parse interactions
```python
import pandas as pd
import numpy as np
ligands = pd.read_csv('ligands2.tsv', sep='\t')
proteins = pd.read_csv('targets2.tsv', sep='\t')
actives = pd.read_csv('interactions_active2.tsv', sep='\t')
inactives = pd.read_csv('interactions_inactives2.tsv', sep='\t')
ligand_dict = {}
for index, item in ligands.iterrows():
ligand_dict[item['InChI Key']] = item['Canonical SMILES']
protein_dict = {}
for index, item in proteins.iterrows():
protein_dict[item['UniProt ID']] = item['FASTA Sequence']
def rehydrate_interactions(x):
uniprot_id = x['UniProt ID']
inchi_id = x['InChI Key']
smile = ligand_dict[inchi_id]
fasta = protein_dict[uniprot_id]
return smile, fasta
actives[["smiles", "seq"]] = actives.apply(rehydrate_interactions, axis=1, result_type="expand")
inactives[["smiles", "seq"]] = inactives.apply(rehydrate_interactions, axis=1, result_type="expand")
actives = actives.drop(columns=['UniProt ID', 'InChI Key'])
inactives = inactives.drop(columns=['UniProt ID', 'InChI Key'])
actives['Y_binary'] = len(actives) * [1]
inactives['Y_binary'] = len(inactives) * [0]
merged = pd.concat([actives, inactives], axis=0)
merged = merged[merged['Unit'] == 'nM']
merged = merged.drop(columns=['Unit'])
merged = merged.reset_index()
merged = merged.drop(columns=['index'])
merged['Value'] = merged['Value'].map(lambda x: x.strip('>').strip(' ').strip('<'))
merged['Value'] = merged['Value'].map(lambda x: x.split(" - ")[0])
merged['Value'] = pd.to_numeric(merged['Value'], errors='coerce')
merged = merged.rename(columns={"Value": "Y_affinity"})
merged = merged[merged['Y_affinity'] != 0]
merged['Y_log_affinity'] = 6 - np.log(merged['Y_affinity']) / np.log(10)
merged = merged[merged['Y_log_affinity'] >= 0]
merged = merged[merged['Y_log_affinity'] <= 10]
shuffled = merged.sample(frac=1)
shuffled = shuffled.reset_index()
shuffled = shuffled.drop(columns=['index'])
```
4. Convert SMILES to [SELFIES](https://github.com/aspuru-guzik-group/selfies)
```python
from datasets import Dataset, DatasetDict
from datasets import load_dataset
import selfies
dataset = Dataset.from_pandas(shuffled, split='train')
def smiles_to_selfies(dataset):
try:
return {"selfies": selfies.encoder(dataset["smiles"])}
except selfies.EncoderError:
return {"selfies": None}
dataset_selfies = dataset.map(smiles_to_selfies)
dataset_selfies = dataset_selfies.filter(lambda dataset: dataset["selfies"] != None)
```
5. 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 = dataset_selfies.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('glass_protein_embeddings.pkl', "wb") as fOut:
pickle.dump(protein_embeddings, fOut, protocol=pickle.HIGHEST_PROTOCOL)
# Ligand embeddings
all_selfies_shortened = dataset_selfies.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('glass_ligand_embeddings.pkl', "wb") as fOut:
pickle.dump(ligand_embeddings, fOut, protocol=pickle.HIGHEST_PROTOCOL)
``` |