Update README.md

README.md

@@ -120,4 +120,44 @@ def smiles_to_selfies(dataset):
 
 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)
 ```