update reference

app.py

@@ -34,26 +34,13 @@ st.set_page_config(
         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. 
+        requires models that can 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.
-
-        ## Citation
-
-        Please cite our work using the following reference.
-        """bibtex
-        @article{svensson2024hyperpcm,
-            title={{HyperPCM: Robust Task-Conditioned Modeling of Drug--Target Interactions}},
-            author={Svensson, Emma and Hoedt, Pieter-Jan and Hochreiter, Sepp and Klambauer, G{\"u}nter},
-            journal={Journal of Chemical Information and Modeling},
-            publisher={ACS Publications},
-            year={2024}
-        }
-        """
         '''
     }
     
@@ -77,7 +64,7 @@ def about_page():
         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. 
+        requires models that can 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
@@ -94,7 +81,7 @@ def about_page():
         ### Citation
 
         Please cite our work using the following reference.
-        '''bibtex
+        ```bibtex
         @article{svensson2024hyperpcm,
             title={{HyperPCM: Robust Task-Conditioned Modeling of Drug--Target Interactions}},
             author={Svensson, Emma and Hoedt, Pieter-Jan and Hochreiter, Sepp and Klambauer, G{\"u}nter},
@@ -102,7 +89,7 @@ def about_page():
             publisher={ACS Publications},
             year={2024}
         }
-        '''
+        ```
         """
     )