update paper reference

app.py

@@ -22,12 +22,12 @@ checkpoint_path = os.path.join(base_path, 'checkpoints/lpo/cv2_test_fold6_1402/m
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 st.set_page_config(
-    page_title='HyperDTI',
+    page_title='HyperPCM',
     layout='centered',
     menu_items={
         'About': 
         '''
-        # HyperDTI
+        # HyperPCM
         
         HyperNetworks have been established as an effective technique to achieve fast adaptation of parameters for 
         neural networks. Recently, HyperNetwork predictions conditioned on descriptors of tasks have improved 
@@ -46,11 +46,11 @@ st.set_page_config(
     
 )
 
-st.title('HyperDTI: Robust Task-Conditioned Modeling of Drug-Target Interactions\n')
+st.title('HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions\n')
 st.markdown('')
 st.markdown(
     """
-    🧬 Github: [ml-jku/hyper-dti](https://https://github.com/ml-jku/hyper-dti)    📝 NeurIPS 2022 AI4Science workshop paper: [OpenReview](https://openreview.net/forum?id=dIX34JWnIAL); TBA in JCIM. \n
+    🧬 Github: [ml-jku/hyper-dti](https://https://github.com/ml-jku/hyper-dti)    📝 Read the paper in: [JCIM](https://pubs.acs.org/doi/10.1021/acs.jcim.3c01417); or the NeurIPS 2022 AI4Science workshop version at [OpenReview](https://openreview.net/forum?id=dIX34JWnIAL) \n
     """
 )
 
@@ -74,7 +74,7 @@ def about_page():
         """
     )
 
-    st.image('figures/hyper-dti.png', caption='Overview of HyperPCM architecture.', use_column_width='always')
+    st.image('figures/hyper-dti.png', caption='Overview of the HyperPCM architecture.', use_column_width='always')
     
 
 def retrieval():