Update README.md

README.md

@@ -1,14 +1,14 @@
----
-title: Drug Discovery App
-emoji: 👀
-colorFrom: pink
-colorTo: red
-sdk: gradio
-sdk_version: 5.33.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
+---
+title: Drug Discovery App
+emoji: 👀
+colorFrom: pink
+colorTo: red
+sdk: gradio
+sdk_version: 5.33.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
 # Comprehensive Drug Discovery Workflow
 
 **[Live Application on Hugging Face Spaces](https://huggingface.co/spaces/alidenewade/drug-discovery-app)**
@@ -42,7 +42,7 @@ The workflow is divided into three main steps:
 * **Automated Model Training**: Train a suite of standard and advanced regression models to predict `pIC50` values. The models include:
     * Linear Regression, Ridge, and Lasso
     * Random Forest and Gradient Boosting Regressors
-    * [cite_start]Optional powerful models like XGBoost, LightGBM, and CatBoost 
+    * Optional powerful models like XGBoost, LightGBM, and CatBoost 
 * **Model Comparison**: Evaluate and rank the models based on performance metrics such as R-squared (`R²`), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE).
 * **In-depth Analysis**: For any selected model, you can visualize:
     * **Validation Plots**: Including actual vs. predicted values and residual plots to diagnose model fit.
@@ -76,29 +76,29 @@ Follow these steps to perform a full drug discovery cycle:
 ## 🔧 Technical Setup
 
 ### Backend & Framework
-* [cite_start]**Application Framework**: The user interface is built with **Gradio**.
-* [cite_start]**Core Libraries**: The application relies heavily on `pandas` for data manipulation, `scikit-learn` for machine learning, and `RDKit` for cheminformatics.
+* **Application Framework**: The user interface is built with **Gradio**.
+* **Core Libraries**: The application relies heavily on `pandas` for data manipulation, `scikit-learn` for machine learning, and `RDKit` for cheminformatics.
 * **Cheminformatics Tools**:
-    * [cite_start]`chembl_webresource_client` is used to interface with the ChEMBL database.
-    * [cite_start]`padelpy` provides a Python wrapper for the **PaDEL-Descriptor** software to calculate molecular fingerprints.
+    * `chembl_webresource_client` is used to interface with the ChEMBL database.
+    * `padelpy` provides a Python wrapper for the **PaDEL-Descriptor** software to calculate molecular fingerprints.
 
 ### System & Python Dependencies
 
 To run this application locally, you will need the following:
 
 * **System-level packages** as defined in `drugDisc_packages.txt`:
-    * [cite_start]`default-jre` / `openjdk-17-jdk` (Java is required for PaDEL-Descriptor) 
-    * [cite_start]`wget` and `unzip` 
+    * `default-jre` / `openjdk-17-jdk` (Java is required for PaDEL-Descriptor) 
+    * `wget` and `unzip` 
 
 * **Python packages** as defined in `drugDisc_requirements.txt`:
-    * [cite_start]`gradio` 
-    * [cite_start]`pandas`, `numpy`, `scipy` 
-    * [cite_start]`matplotlib`, `seaborn` 
-    * [cite_start]`scikit-learn` 
-    * [cite_start]`rdkit` 
-    * [cite_start]`chembl_webresource_client` 
-    * [cite_start]`padelpy` 
-    * [cite_start]`xgboost`, `lightgbm`, `catboost` 
+    * `gradio` 
+    * `pandas`, `numpy`, `scipy` 
+    * `matplotlib`, `seaborn` 
+    * `scikit-learn` 
+    * `rdkit` 
+    * `chembl_webresource_client` 
+    * `padelpy` 
+    * `xgboost`, `lightgbm`, `catboost` 
 
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