From 661d4445361feb5e5baeabf1c45ebd1ffe54705d Mon Sep 17 00:00:00 2001
From: Mayk Caldas <maykcaldas@gmail.com>
Date: Tue, 16 Apr 2024 11:16:47 -0400
Subject: [PATCH] Publication release (#10)

Updated the version, removed the 'on-going study' warning and updated the reference
---
 README.md              | 25 +++++++++++--------------
 package.json           |  3 ++-
 src/components/App.vue |  9 ++-------
 3 files changed, 15 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index 5954824..5ecf0c4 100644
--- a/README.md
+++ b/README.md
@@ -47,22 +47,19 @@ The button "Expand ▶️" can be used to get information about the prediction o
   <img style="width: 50%;" src="https://github.com/ur-whitelab/mol.dev/blob/main/models/rnn-fit.png?raw=true" alt="Mol.dev implemented model parity plot with metrics">
 </p>
 
-# Important Note
-
-Please, note that this is an ongoing research. The results showed here are results of models that are still being developed and improved.
-
 # Citation
 
-Please, cite [Ramos et al.](https://arxiv.org/abs/2307.05318):
+Please, cite [Ramos et al.](https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00217a):
 ```
-@misc{ramos2023solubility,
-  doi = {10.48550/ARXIV.2307.05318},
-  url = {https://arxiv.org/abs/2307.05318},
-  author = {Ramos,  Mayk Caldas and White,  Andrew D.},
-  keywords = {Chemical Physics (physics.chem-ph),  Machine Learning (cs.LG),  FOS: Physical sciences,  FOS: Physical sciences,  FOS: Computer and information sciences,  FOS: Computer and information sciences},
-  title = {Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks},
-  publisher = {arXiv},
-  year = {2023},
-  copyright = {Creative Commons Attribution 4.0 International}
+@Article{ramos2023solubility,
+author ="Ramos, Mayk Caldas and White, Andrew D.",
+title  ="Predicting small molecules solubility on endpoint devices using deep ensemble neural networks",
+journal  ="Digital Discovery",
+year  ="2024",
+pages  ="-",
+publisher  ="RSC",
+doi  ="10.1039/D3DD00217A",
+url  ="http://dx.doi.org/10.1039/D3DD00217A",
 }
+
 ```
\ No newline at end of file
diff --git a/package.json b/package.json
index dc1e656..c8b8bf4 100644
--- a/package.json
+++ b/package.json
@@ -1,6 +1,6 @@
 {
   "name": "mol-dashboard",
-  "version": "0.5.0",
+  "version": "1.0.0",
   "description": "molecule dashboard",
   "browserslist": "> 0.5%, last 2 versions, not dead",
   "scripts": {
@@ -23,6 +23,7 @@
     "@parcel/transformer-inline-string": "^2.5.0",
     "@parcel/transformer-sass": "^2.5.0",
     "@parcel/transformer-vue": "^2.5.0",
+    "@vue/cli-service": "^5.0.8",
     "eslint": "^8.2.0",
     "eslint-plugin-vue": "^8.0.3"
   },
diff --git a/src/components/App.vue b/src/components/App.vue
index e654b0b..bb88aee 100644
--- a/src/components/App.vue
+++ b/src/components/App.vue
@@ -14,9 +14,6 @@
         </div>
       </div>
     </section>
-    <section>
-      <version :version="version"></version>
-    </section>
     <section>
       <div ref="inputcontainer" class="container">
         <div class="columns is-centered">
@@ -75,9 +72,9 @@
           </div>
           <div class="block column">
               <p>
-                Please, cite our work: <reference reflink="https://arxiv.org/abs/2307.05318"
+                Please, cite our work: <reference reflink="https://pubs.rsc.org/en/content/articlelanding/2024/dd/d3dd00217a"
                   reftitle="Predicting small molecules solubilities on endpoint devices using deep ensemble neural networks"
-                  journal="ArXiv" year="2023"></reference>
+                  journal="Digital Discovery" year="2024"></reference>
               </p>
           </div>
         </div>
@@ -87,7 +84,6 @@
 </template>
 
 <script>
-import Version from "./Version.vue";
 import MolInput from "./MolInput";
 import TfPrediction from "./results/TfPrediction";
 import Reference from "./Reference";
@@ -100,7 +96,6 @@ export default {
     TfPrediction,
     MolInput,
     Reference,
-    Version,
     SmilesViewer
   },
   data() {