add references

app.py

@@ -338,7 +338,32 @@ def references():
         '''
         ## References
 
-        This page will contain all references to related work.
+        Schmidhuber, J., “Learning to control fast-weight memories: An alternative to dynamic recurrent networks.” Neural Computation, 1992.
+
+        Davis, M. I., et al. "Comprehensive analysis of kinase inhibitor selectivity." Nature Biotechnology 29.11 (2011): 1046-1051.
+        
+        Ha, D., et al. “HyperNetworks”. ICLR, 2017.
+        
+        Lenselink, E. B., et al. "Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set." Journal of Cheminformatics 9.1 (2017): 1-14.
+
+        Alley, E. C., et al. "Unified rational protein engineering with sequence-based deep representation learning." Nature Methods 16.12 (2019): 1315-1322.
+        
+        Chang, O., et al., “Principled weight initialization for hypernetworks.” ICLR, 2019.
+
+        Heinzinger, M., et al. "Modeling aspects of the language of life through transfer-learning protein sequences." BMC Bioinformatics 20.1 (2019): 1-17.
+        
+        Winter, R., et al. "Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations." Chemical Science 10.6 (2019): 1692-1701.
+        
+        Fabian, B., et al. "Molecular representation learning with language models and domain-relevant auxiliary tasks." Workshop for ML4Molecules (2020).
+    
+        Elnaggar, A., et al. "ProtTrans: Toward understanding the language of life through self-supervised learning." IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (2021): 7112–7127.
+        
+        Rives, A., et al. "Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences." Proceedings of the National Academy of Sciences 118.15 (2021): e2016239118.
+        
+        Kim, P. T., et al. "Unsupervised Representation Learning for Proteochemometric Modeling." International Journal of Molecular Sciences 22.23 (2021): 12882.
+        
+        Schimunek, J., et al., “Context-enriched molecule representations improve few-shot drug discovery.” ICLR, 2023.
+
         '''
     )