--- a
+++ b/README.md
@@ -0,0 +1,26 @@
+# Using Pretrained Machine Learning Models to Predict Peptide Stability Profile in Simulated Gastric/Intestinal Fluids
+
+Publication: *DOI:* [article link](https://doi.org/10.1016/j.ijpharm.2023.122643)
+
+Data: [FigShare](https://doi.org/10.6084/m9.figshare.25941580)
+
+Environment: Python 3.7.7
+
+Dependancies:
+- scikit-learn: 0.24.2
+- py-xgboost: 1.3.3
+- rdkit: 2020.03.3.0
+- pandas: 1.3.0
+- numpy: 1.20.3
+
+## To Predict
+
+In order to predict peptide stability, the structure of the peptide, represented in *isomeric SMILES* notation, should be prepared first.
+
+1. Edit the .csv file in the folder to fill in peptide information (Multiple prediction are supported by adding extra rows). The last two columns 'Stability_in_SIF' and 'Stability_in_SGF' can be left empty and will be filled automatically.
+
+2. Run the code in the jupyter notebook.
+
+3. The result will be displayed on the notebook and also saved into the .csv file.
+
+