Update README.md

README.md

@@ -9,35 +9,35 @@ This application demonstrates a drug discovery pipeline that allows users to:
 * Predict select ADMET properties for the generated molecules using ADMET-AI.
 
 ## Features
-* Molecule Generation:
+* **Molecule Generation**:
   * Generates up to 3 beta-lactam molecules at a time.
   * Users can adjust the creativity (temperature) of the generation process. Higher number leads to more diverse output. 
   * Generated molecules are named 'Mol01' to 'Mol03'.
-* Molecule Viewing:
+* **Molecule Viewing**:
   * Displays molecule structures using Streamlit.
   * Option to view molecules as SMILES or SAFE encodings.
-* ADMET Property Prediction:
+* **ADMET Property Prediction**:
   * Integrates ADMET-AI to predict select properties. 
   * Displays predicted properties of each molecule.
 
 ## How to Use the App
 1. Set Generation Parameters:
-  * Use the sidebar to adjust the creativity (temperature) slider.
-  * Select the number of molecules to generate (maximum of 3).
+    * Use the sidebar to adjust the creativity (temperature) slider.
+    * Select the number of molecules to generate (maximum of 3).
 2. Generate Molecules:
-  * Click the 'Generate Molecules' button.
-  * Generated molecules will appear with their structures, strings, and predicted ADMET properties.
+    * Click the 'Generate Molecules' button.
+    * Generated molecules will appear with their structures, strings, and predicted ADMET properties.
 
 
 
 ## Technical Details
-* Generative Model: Uses the model: 'seyonec/PubChem10M_SMILES_BPE_450k' fine-tuned on beta-lactam structures.
-* ADMET Predictions: Uses the ADMET-AI library to predict molecular properties.
-* Visualization: Employs RDKit and SAFE encoding for molecule rendering.
-* Frameworks and Libraries:
-  * Streamlit for the web interface.
-  * Transformers library for model loading and generation.
-  * RDKit for cheminformatics.
+* **Generative Model**: Uses the model: 'seyonec/PubChem10M_SMILES_BPE_450k' fine-tuned on beta-lactam structures.
+* **ADMET Predictions**: Uses the ADMET-AI library to predict molecular properties.
+* **Visualization**: Employs RDKit and SAFE encoding for molecule rendering.
+* **Frameworks and Libraries**:
+  * **Streamlit** for the web interface.
+  * **Transformers** library for model loading and generation.
+  * **RDKit** for cheminformatics.
 
 
 
@@ -56,16 +56,16 @@ Zdrazil B, Felix E, Hunter F, et al. The ChEMBL Database in 2023: a drug discove
 . doi:10.1093/nar/gkad1004
 https://www.ebi.ac.uk/chembl/
 
-### SAFE Encoding
+### SAFE Encoding:
 This project uses the SAFE Encoding framework, licensed under the Apache License 2.0.
 Noutahi E, Gabellini C, Craig M, Lim JS, Tossou P. Gotta be SAFE: A New Framework for Molecular Design. arXiv preprint arXiv:2310.10773, 2023.
 https://github.com/datamol-io/safe
 
-### This project utilizes the ADMET-AI platform for predicting ADMET properties. ADMET-AI is licensed under the MIT License.
+### This project utilizes the ADMET-AI platform for predicting ADMET properties:
 Swanson K, Walther P, Leitz J, et al. ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries. bioRxiv. 2023. doi:10.1101/2023.12.28.573531
 https://admet.ai.greenstonebio.com/
 
-### This project uses RDKit: Open-source cheminformatics software.
+### This project uses RDKit: 
 RDKit: Open-source cheminformatics. https://www.rdkit.org