Update README.md

README.md

@@ -185,7 +185,7 @@ then load, featurize, split, fit, and evaluate the catboost model
         predictions=preds["cat_boost_classifier::BBB+/BBB-"])
 
 
-## About the DB3B
+## Aqueous Solubility Data Curation
 
 ### Overview
 
@@ -202,6 +202,59 @@ merge.py
 descriptors.py
 Further information about curation process can be found in the associated manuscript.
 
+## Examples
+
+### data-preprocess.py
+This file converts 2 example sub-datasets (25 instances from raw forms of dataset-A[1] and dataset-H[6]) 
+which are then converted into a standardized format. (This is an example how to preprocess datasets. 
+The preporcessed data files already in the data folder.)
+
+inputs:
+
+raw-dataset-A.csv (various solubility metrics (g/L, mg/L..) with Name and CAS Number)
+raw-dataset-H.csv (has solubility values(LogS) with SLN representations)
+
+outputs:
+
+dataset-A.csv
+dataset-H.csv
+
+Note
+To apply this method to your own dataset, perform the following steps:
+
+Check the available properties, representations, and solubility units of your dataset
+Select the suitable preprocessing methods from the "preprocess.py" module.
+
+
+### data-curation.py
+This file curates, i.e., merges datasets, selects most reliable values among multiple occurences, and adds 2D descriptors from 9 different standardized datasets that are obtained after the pre-processing step.
+
+inputs:
+
+dataset-A.csv [1]
+dataset-B.csv [2]
+dataset-C.csv [3]
+dataset-D.csv [4]
+dataset-E.csv [5]
+dataset-F.csv [6]
+dataset-G.csv [7]
+dataset-H.csv [6]
+dataset-I.csv [8]
+
+outputs:
+
+dataset_curated.csv
+
+Note
+To apply this method, your input dataset should be in the standardized format (output of preprocessing) having following columns:
+
+ID
+Name
+InChI
+InChIKey
+SMILES
+Solubility
+Prediction
 
 ### Data splits
 The original AqSoDB dataset does not define splits, so here we have used the `Realistic Split` method described