Updated docs index

docs/README.md

@@ -1,7 +1,54 @@
 # Useful Tips on How to Build the Documentation
 
-This document provides useful tips on how to build the documentation for the project.
+## Steps
 
+1. Install `sphinx` and `sphinx_rtd_theme`:
+   ```bash
+   pip install sphinx sphinx_rtd_theme
+   ```
+2. Create a `docs` directory in the root of the project:
+   ```bash
+    mkdir docs
+    ```
+3. Create the documentation structure:
+    ```bash
+    cd docs
+    sphinx-quickstart
+    ```
+    - Answer the questions as follows:
+      - Separate source and build directories (y/n) [n]: n
+      - Project name: PROTAC-Degradation-Predictor
+      - Author name(s): Your Name
+      - Project version: v1.0.1
+    ```
+4. Edit the `conf.py` file:
+    - Add the following lines:
+      ```python
+      import os
+      import sys
+      sys.path.insert(0, os.path.abspath('..'))
+      ```
+    - Add the following line to the `extensions` list:
+      ```python
+      'sphinx.ext.autodoc',
+      ```
+    - Check the current `conf.py` file in this repository for more details.
+5. Create modules rst files: `sphinx-apidoc -o docs/source/ ./protac_degradation_predictor`
+6. To include modules in the documentation, in `index.rst`, add the following line: `source/modules`
+7. Setup the gh-pages branch:
+    ```bash
+    git checkout --orphan gh-pages
+    git reset --hard
+    git commit --allow-empty -m "Init"
+    git push origin gh-pages
+    git checkout main
+    ```
+8. Generate the specific workflow Action file:
+    - See file `.github/workflows/gh-pages.yml` in this repository.
+
+## Miscellaneous
+
+- Useful [guide](https://olgarithms.github.io/sphinx-tutorial/docs/7-hosting-on-github-pages.html)
 - Automatically create modules rst files: `sphinx-apidoc -o source/ ../protac_degradation_predictor`
   - Then, in `index.rst`, add the following line: `source/modules`
 - Build the documentation: `make clean ; make html`

docs/index.rst

@@ -1,18 +1,110 @@
 .. PROTAC-Degradation-Predictor documentation master file, created by
-   sphinx-quickstart on Mon Aug 19 11:16:51 2024.
+   sphinx-quickstart on Mon Aug 23 17:31:15 2021.
    You can adapt this file completely to your liking, but it should at least
    contain the root `toctree` directive.
 
-PROTAC-Degradation-Predictor documentation
-==========================================
+===========================================================
+PROTAC-Degradation-Predictor: Documentation and Overview
+===========================================================
 
-Add your content using ``reStructuredText`` syntax. See the
-`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
-documentation for details.
+**PROTAC-Degradation-Predictor** is a Python package designed to predict the activity of PROTAC molecules using advanced machine learning techniques. The tool aims to assist researchers in evaluating the potential effectiveness of PROTACs, a novel class of drugs that target protein degradation.
 
+.. .. image:: https://yourimageurl.com/logo.png  # Add your project's logo or any relevant image
+..    :align: center
+
+Introduction
+============
+
+PROTACs (Proteolysis Targeting Chimeras) are a class of molecules that induce the degradation of specific proteins. This package allows researchers to predict the activity of PROTACs by leveraging a variety of machine learning models, including XGBoost and PyTorch-based pretrained neural networks.
+
+The primary functionalities of this package include:
+- Predicting PROTAC activity using different machine learning models.
+- Accessing curated datasets for training and evaluation.
+- Hyperparameter tuning and model training using Optuna.
+
+Features
+========
+
+- **Machine Learning Models**: Utilize XGBoost, PyTorch, and scikit-learn models to predict PROTAC activity (refer to the :func:`protac_degradation_predictor.get_protac_active_proba` function).
+- **Dataset Handling**: Load and manage datasets specific to PROTAC research (refer to the :func:`protac_degradation_predictor.data_utils.load_curated_dataset` function).
+- **Customizability**: Tune model hyperparameters and experiment with different model configurations (refer to the :func:`protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training` function).
+
+Quickstart
+==========
+
+To get started with PROTAC-Degradation-Predictor, follow these steps:
+
+1. **Installation**:
+   Install the package using pip:
+   
+   .. code-block:: bash
+   
+      git clone https://github.com/ribesstefano/PROTAC-Degradation-Predictor.git
+      cd PROTAC-Degradation-Predictor
+      pip install .
+
+2. **Basic Usage**:
+   Here's an example of how to predict PROTAC activity:
+   
+   .. code-block:: python
+   
+      from protac_degradation_predictor import get_protac_active_proba
+      
+      smiles = "CC(C)C1=CC=C(C=C1)C2=NC3=CC=CC=C3C(=O)N2"
+      e3_ligase = "Q9Y6K9"
+      target_uniprot = "P04637"
+      cell_line = "HCT116"
+      
+      prediction = get_protac_active_proba(
+          protac_smiles=smiles,
+          e3_ligase=e3_ligase,
+          target_uniprot=target_uniprot,
+          cell_line=cell_line,
+          device='cpu',
+          use_models_from_cv=False,
+          use_xgboost_models=True,
+          study_type='standard'
+      )
+      print(prediction)
+
+For more detailed usage and customization, refer to the relevant sections below.
+
+Contents
+========
 
 .. toctree::
-   :maxdepth: 4
-   :caption: Contents:
+   :maxdepth: 2
+   :caption: Documentation Contents:
+
+   source/modules
+   source/protac_degradation_predictor
+   source/protac_degradation_predictor.optuna_utils
+   source/protac_degradation_predictor.protac_dataset
+   source/protac_degradation_predictor.pytorch_models
+
+Getting Help
+============
+
+If you encounter any issues or have questions, please refer to the following resources:
+- **Documentation**: Full API documentation and user guide.
+- **GitHub Issues**: Report bugs or request features on the [GitHub Issues](https://github.com/ribesstefano/PROTAC-Degradation-Predictor/issues) page.
+- **Contributing**: Learn how to contribute to the project by reading our [Contribution Guidelines](https://github.com/ribesstefano/PROTAC-Degradation-Predictor/blob/main/CONTRIBUTING.md).
+
+License
+=======
+
+This project is licensed under the MIT License. See the [LICENSE](https://github.com/ribesstefano/PROTAC-Degradation-Predictor/blob/main/LICENSE) file for details.
+
+About
+=====
+
+**Author**: Stefano Ribes
+
+**Version**: v1.0.1
+
+Built with Sphinx using the [Read the Docs theme](https://sphinx-rtd-theme.readthedocs.io/).
+
+----------
+
+*This documentation was last updated on August 19, 2024.*
 
-   source/modules