v1.0.0: refactoring complete!

README.md

@@ -1,10 +1,8 @@
-# ReBADD-SE (under construction)
+# ReBADD-SE: Multi-objective Molecular Optimisation using SELFIES Fragment and Off-Policy Self-critical Sequence Training
 This is the repository for ReBADD-SE, a multi-objective molecular optimization model that designs a molecular structures in the format of SELFIES.
 For more details, please refer to our [paper](https://www.sciencedirect.com/science/article/pii/S0010482523001865).
 
-**Notice** We are currently in the process of updating the repository to enhance the usability of ReBADD-SE. We apologize for any inconvenience this may cause during the update. The update is expected to be completed within this week, and we appreciate your patience. 
-
-- Latest update: 24 Jan 2024
+- Latest update: 26 Jan 2024
 
 
 
@@ -21,23 +19,23 @@ Task Descriptions
 - TASK1: ReBADD-SE for GSK3b, JNK3, QED, and SA (frag-level)
 - TASK3: ReBADD-SE for BCL2, BCLXL, and BCLW (frag-level)
 - TASK4: ReBADD-SE for BCL2, BCLXL, and BCLW (char-level)
-- TASK7: SELFIES Collapse Analaysis between ReBADD-SE (frag, char-level) and GA+D
+- TASK7: SELFIES Collapse Analaysis between ReBADD-SE (frag-level) and GA+D
 
 
 
 Notebook Descriptions
 ----
 ## 0_preprocess_data.ipynb
+- *(Important!)* Before starting any TASK, please first run the scripts in the directory 'data/chembl' or 'data/zinc15'
 - Read the training data
 - Preprocess the data for model training
-- The preprocessed data are stored in the 'processed_data' directory
 
 ## 1_pretraining.ipynb
 - Read the training data
 - The generator learns the grammar rules of SELFIES
 
 ## 2_optimize+{objectives}.ipynb
-- (Important!) Please check first the 'ReBADD_config.py' in which a reward function have to be defined appropriately
+- *(Important!)* Please check first the 'ReBADD_config.py' in which a reward function have to be defined appropriately
 - Load the pretrained generator
 
 ## 3_checkpoints+{objectives}.ipynb