Update README.md

README.md

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----
-license: cc0-1.0
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+---
+license: cc0-1.0
+task_categories:
+- tabular-regression
+language:
+- en
+pretty_name: MolData
+size_categories:
+- 1M<n<10M
+tags:
+- drug discovery
+- bioassay
+dataset_summary: >-
+ A comprehensive disease and target-based dataset with 1.4 million molecules, collected from PubChem
+ to accelerate molecular machine learning for better drug discovery.
+citation: >-
+ @article{KeshavarziArshadi2022,
+  title = {MolData,  a molecular benchmark for disease and target based machine learning},
+  volume = {14},
+  ISSN = {1758-2946},
+  url = {http://dx.doi.org/10.1186/s13321-022-00590-y},
+  DOI = {10.1186/s13321-022-00590-y},
+  number = {1},
+  journal = {Journal of Cheminformatics},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Keshavarzi Arshadi,  Arash and Salem,  Milad and Firouzbakht,  Arash and Yuan,  Jiann Shiun},
+  year = {2022},
+  month = mar 
+ }
+
+ 
+---
+
+# MolData
+
+[MolData](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-022-00590-y) is a comprehensive disease and target-based dataset collected from PubChem.
+The dataset contains 1.4 million unique molecules, and it is one the largest efforts to date for democratizing the molecular machine learning.
+
+
+## Preprocessing
+
+We utilized the raw data uploaded on [Github](https://github.com/LumosBio/MolData) and performed several preprocessing:
+1. Sanitize the molecules using RDKit and MolVS (standardize SMILES format)
+2. Rename the columns
+3. Split the dataset (train, test, validation)
+
+If you would like to try these processes with the original dataset, 
+please follow the instructions in the [Preprocessing Script.py](address) file located in our MolData repository.
+