Update README.md

README.md

@@ -20,4 +20,104 @@ dataset_info:
   download_size: 161743110
   dataset_size: 606248442.8293403
 ---
-# From [GLASS GPCR database](https://zhanggroup.org/GLASS/), converted to SELFIES
+# 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)
+```