Update README.md

README.md

@@ -5,6 +5,21 @@ tags:
 - chemistry
 - toxicology
 pretty_name: AttentiveSkin
+dataset_summary: >-
+ They compiled GHS dataset comprising 731 Corr., 1283 Irrit., and 1205 Neg. samples 
+ from 6 governmental databases and 2 external datasets.
+citation: >-
+  @article{,
+    author = {Zejun Huang, Shang Lou, Haoqiang Wang, Weihua Li, Guixia Liu, and Yun Tang},
+    doi = {10.1021/acs.chemrestox.3c00332},
+    journal = {Journal of Chemical Information and Modeling},
+    number = {2},
+    title = {AttentiveSkin: To Predict Skin Corrosion/Irritation Potentials of Chemicals via Explainable Machine Learning Methods},
+    volume = {37},
+    year = {2024},
+    url = {https://pubs.acs.org/doi/10.1021/acs.chemrestox.3c00332?ref=pdf},
+    publisher = {ACS publications}
+  }
 size_categories:
 - 1K<n<10K
 config_names:
@@ -164,10 +179,6 @@ The interpretable prediction containing attetion weights will be placed in .html
 Results of the two binary tasks (Corr vs Neg, Irrit vs Neg) are generated separately.
 Click the button "Output" to select the directory to store the prediction results.
 
-## Data splits
-The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
-in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166).
-
 ## Citation
 Cite this:
 Chem. Res. Toxicol. 2024, 37, 2, 361–373