Update README.md

README.md

@@ -10,6 +10,31 @@ tags:
 pretty_name: Assay-Interfering-Compounds Finder
 size_categories:
 - 1M<n<10M
+dataset_summary: >-
+  The assay-interfering-compounds finder consists of 17 different datasets. The datasets are uploaded after molecular sanitization using RDKit and MolVS.
+citation: >-
+  @article{Boldini2024,
+    title = {Machine Learning Assisted Hit Prioritization for High Throughput Screening in Drug Discovery},
+    ISSN = {2374-7951},
+    url = {http://dx.doi.org/10.1021/acscentsci.3c01517},
+    DOI = {10.1021/acscentsci.3c01517},
+    journal = {ACS Central Science},
+    publisher = {American Chemical Society (ACS)},
+    author = {Boldini,  Davide and Friedrich,  Lukas and Kuhn,  Daniel and Sieber,  Stephan A.},
+    year = {2024},
+    month = mar 
+  }
+config_names:
+- Boldini2024
+dataset_info:
+  config_name: Boldini2024
+  data_files:
+    - GPCR_sanitized.csv
+    - GPCR2_sanitized.csv
+    - GPCR3_sanitized.csv
+
+  
+
 
 ---
 # Boldini2024 (Assay-Interfering-Compounds Finder)
@@ -17,8 +42,8 @@ size_categories:
 In this study, they present the first data-driven approach to simultaneously detect assay interferents and prioritize true bioactive compounds. 
 Their method enables false positive and true positive detection without relying on prior screens or assay interference mechanisms, making it applicable to any high throughput screening campaign.
 
-The datasets uploaded to our Hugging Face repository have been sanitized and split from the original dataset. 
-If you would like to try these processes with the original dataset, please follow the instructions in the [Processing Script.py]() file located in the maomlab/Boldini2024.
+The datasets uploaded to our Hugging Face repository have been sanitized using RDKit and MolVS.
+If you want to try these processes with the original dataset, please follow the instructions in the [Processing Script.py]() file in the maomlab/Boldini2024.
 
 
 # Citation