Update README.md

README.md

@@ -187,23 +187,24 @@ then load, featurize, split, fit, and evaluate the catboost model
 
 ## About the DB3B
 
-### Features of *B3DB*
+### Overview
 
-1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules
-    (to the best of our knowledge, as of February 25, 2021).
+This repository has been developed in order to curate various aqueous solubility datasets into a broad and extensive dataset called AqSolDB.
 
-2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if
-    available. Otherwise, canonical SMILES are used.
+The curation process in this work can be accomplished by executing two python scripts in the given sequence:
 
-3. Characterization of uncertainty of experimental measurements by grouping the collected molecular
-    data records.
+data-preprocess.py - for pre-processing the raw data set to a standardized format
+data-curation.py - for merging the standardized datasets, assigning reliability lables and adding 2D descriptors
+These two python scripts call upon functions from other python modules that are defined in:
 
-4. Extended datasets for numerical and categorical data with precomputed physicochemical properties
-    using [mordred](https://github.com/mordred-descriptor/mordred).
+preprocess.py
+merge.py
+descriptors.py
+Further information about curation process can be found in the associated manuscript.
 
 
 ### Data splits
-The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
+The original AqSoDB 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