Update About.py

About.py

@@ -24,7 +24,7 @@ def run():
         layout='wide'
     )
 
-    st.write("## [Exploiting Pretrained Biochemical Language Models for Targeted Drug Design](https://doi.org/10.1093/bioinformatics/btac482)")
+    st.write("## [Exploiting Pretrained Biochemical Language Models for Targeted Drug Design](https://arxiv.org/abs/2209.00981)")
     #st.sidebar.title("Model Demos")
     st.sidebar.success("Select a model demo above.")
 
@@ -48,7 +48,7 @@ def run():
         biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate
         two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation
         and (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target-specific training. We
-        also compare two decoding strategies to generate compounds: beamsearch and sampling.
+        also compare two decoding strategies to generate compounds: beam search and sampling.
 
         **Results:** The results show that the warm-started models perform better than a baseline model trained from scratch.
         The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics
@@ -60,13 +60,21 @@ def run():
         **Availability and implementation:** The source code is available at https://github.com/boun-tabi/biochemical-lms-for-drug-design and the materials (i.e., data, models, and outputs) are archived in Zenodo at https://doi.org/10.5281/zenodo.6832145. 
         ### Citation
         ```bibtex
-        @article{10.1093/bioinformatics/btac482,
-            author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan},
-            title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}",
-            journal = {Bioinformatics},
-            year = {2022},
-            doi = {10.1093/bioinformatics/btac482},
-            url = {https://doi.org/10.1093/bioinformatics/btac482}
+        @misc{https://doi.org/10.48550/arxiv.2209.00981,
+          doi = {10.48550/ARXIV.2209.00981},
+          
+          url = {https://arxiv.org/abs/2209.00981},
+          
+          author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün and Özgür, Arzucan},
+          
+          keywords = {Machine Learning (cs.LG), Computation and Language (cs.CL), Biomolecules (q-bio.BM), Quantitative Methods (q-bio.QM), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Biological sciences, FOS: Biological sciences},
+          
+          title = {Exploiting Pretrained Biochemical Language Models for Targeted Drug Design},
+          
+          publisher = {arXiv},
+          
+          year = {2022}
+          
         }
         ```
     """