add dummy encoders for drug and target

app.py

@@ -46,29 +46,68 @@ def about_page():
 
 def display_dti():
     st.markdown('##')
-    smiles = st.text_input('Enter the SMILES of the query drug compound', value='CC(=O)OC1=CC=CC=C1C(=O)O', placeholder='CC(=O)OC1=CC=CC=C1C(=O)O')
+    col1, col2 = st.columns(2)
     
-    if smiles:
-        mol = Chem.MolFromSmiles(smiles)
-        mol_img = Chem.Draw.MolToImage(mol)
-        col1, col2, col3 = st.columns(3)
-        with col1:
-            st.write('')
-        with col2:
+    with col1:
+        st.markdown('### Drug')
+        smiles = st.text_input('Enter the SMILES of the query drug compound', value='CC(=O)OC1=CC=CC=C1C(=O)O', placeholder='CC(=O)OC1=CC=CC=C1C(=O)O')
+        
+        if smiles:
+            mol = Chem.MolFromSmiles(smiles)
+            mol_img = Chem.Draw.MolToImage(mol)
             st.image(mol_img, width = 140)
-        with col3:
-            st.write('')
-        st.markdown('##')
+            
+            selected_encoder = st.selectbox(
+                'Select encoder for drug compound',('None', 'CDDD', 'MolBERT')
+            )
+            if selected_encoder == 'CDDD':
+                from cddd.inference import InferenceModel
+                CDDD_MODEL_DIR = 'checkpoints/CDDD/default_model'
+                cddd_model = InferenceModel(CDDD_MODEL_DIR)
+                embedding = cddd_model.seq_to_emb([smiles])
+                st.write(f'CDDD embedding: {embedding}')
+            elif selected_encoder == 'MolBERT':
+                from molbert.utils.featurizer.molbert_featurizer import MolBertFeaturizer
+                MOLBERT_MODEL_DIR = 'checkpoints/MolBert/molbert_100epochs/checkpoints/last.ckpt'
+                molbert_model = MolBertFeaturizer(MOLBERT_MODEL_DIR, max_seq_len=500, embedding_type='average-1-cat-pooled')
+                embedding = molbert_model.transform([smiles])
+            else: 
+                st.write('No pre-trained version of HyperPCM is available for the chosen encoder.')
 
-        selected_encoder = st.selectbox(
-            'Select encoder for drug compound',('None', 'CDDD')
-        )
-        if selected_encoder == 'CDDD':
-            from cddd.inference import InferenceModel
-            CDDD_MODEL_DIR = 'checkpoints/CDDD/default_model'
-            cddd_model = InferenceModel(CDDD_MODEL_DIR)
-            embedding = cddd_model.seq_to_emb([smiles])
-            st.write(f'CDDD embedding: {embedding}')
+    with col2:
+        st.markdown('### Target')
+        sequence = st.text_input('Enter the amino-acid sequence of the query protein target', value='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA', placeholder='HXHVWPVQDAKARFSEFLDACITEGPQIVSRRGAEEAVLVPIGEWRRLQAAA')
+        
+        if sequence:
+            st.write('Plot of protein to be added soon.')
+            
+            selected_encoder = st.selectbox(
+                'Select encoder for protein target',('None', 'SeqVec', 'UniRep', 'ESM-1b', 'ProtT5')
+            )
+            if selected_encoder == 'SeqVec':
+                from bio_embeddings.embed import SeqVecEmbedder
+                encoder  SeqVecEmbedder()
+                embedding = encoder([sequence])
+                embedding = encoder.reduce_per_protein(embedding)
+                st.write(f'SeqVec embedding: {embedding}')
+            elif selected_encoder == 'UniRep':
+                #from jax_unirep.utils import load_params
+                #params = load_params() 
+                from jax_unirep.featurize import get_reps                               
+                embedding, h_final, c_final = get_reps([sequence])
+                embedding = embedding.mean(axis=0)
+            elif selected_encoder == 'ESM-1b':
+                from bio_embeddings.embed import ESM1bEmbedder
+                encoder = ESM1bEmbedder()
+                embedding = encoder([sequence])
+                embedding = encoder.reduce_per_protein(embedding)
+            elif selected_encoder == 'ProtT5':
+                from bio_embeddings.embed import ProtTransT5XLU50Embedder
+                encoder = ProtTransT5XLU50Embedder()
+                embedding = encoder([sequence])
+                embedding = encoder.reduce_per_protein(embedding)
+            else: 
+                st.write('No pre-trained version of HyperPCM is available for the chosen encoder.')
 
 
 def display_protein():