update menu

app.py

@@ -21,7 +21,33 @@ data_path = os.path.join(base_path, 'data')
 checkpoint_path = os.path.join(base_path, 'checkpoints/lpo/cv2_test_fold6_1402/model_updated.t7')
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-st.set_page_config(layout="centered")
+st.set_page_config(
+    page_title='HyperDTI',
+    layout='centered',
+    menu_items={
+        'Paper': 'https://openreview.net/forum?id=dIX34JWnIAL',
+        'GitHub': 'https://https://github.com/ml-jku/hyper-dti',
+        'Contact': 'mailto:[email protected]',
+        'About': 
+        '''
+        # HyperDTI
+        
+        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 
+        multi-task generalization in various domains, such as personalized federated learning and neural architecture 
+        search. Especially powerful results were achieved in few- and zero-shot settings, attributed to the increased 
+        information sharing by the HyperNetwork. With the rise of new diseases fast discovery of drugs is needed which 
+        requires models that are able to generalize drug-target interaction predictions in low-data scenarios. 
+        
+        In this work, we propose the HyperPCM model, a task-conditioned HyperNetwork approach for the problem of 
+        predicting drug-target interactions in drug discovery. Our model learns to generate a QSAR model specialized on
+        a given protein target. We demonstrate state-of-the-art performance over previous methods on multiple 
+        well-known benchmarks, particularly in zero-shot settings for unseen protein targets. This app demonstrates the 
+        model as a retrieval task of the top-k most active drug compounds predicted for a given query target.
+        '''
+    }
+    
+)
 
 st.title('HyperDTI: Robust Task-Conditioned Modeling of Drug-Target Interactions\n')
 st.markdown('')