Updated README and Conda environment configuration file

.gitignore

@@ -165,5 +165,6 @@ cython_debug/
 data/uniprot2embedding.h5
 data/PROTAC-DB.csv
 data/PROTAC-Pedia.csv
+data/cellosaurus.txt
 logs/
 notebooks/per-protein*

README.md

@@ -1,22 +1,19 @@
-<!-- ![Maturity level-0](https://img.shields.io/badge/Maturity%20Level-ML--0-red)
+![Maturity level-0](https://img.shields.io/badge/Maturity%20Level-ML--0-red)
+<a href="https://colab.research.google.com/github/ribesstefano/PROTAC-Degradation-Predictor/blob/main/notebooks/protac_degradation_predictor_tutorial.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
 
-# PROTAC-Degradation-Predictor -->
 
-<p align="center">
-  <img src="https://img.shields.io/badge/Maturity%20Level-ML--0-red" alt="Maturity level-0">
-</p>
+# PROTAC-Degradation-Predictor
 
-<h1 align="center">PROTAC-Degradation-Predictor</h1>
-
-<p align="center">
-  A machine learning-based tool for predicting PROTAC protein degradation activity.
-</p>
+A machine learning-based tool for predicting PROTAC protein degradation activity.
 
 ## 📚 Table of Contents
 
 - [Data Curation](#-data-curation)
 - [Installation](#-installation)
 - [Usage](#-usage)
+- [Training](#-training)
+- [Citation](#-citation)
+- [License](#-license)
 
 ## 📝 Data Curation
 
@@ -36,12 +33,14 @@ The package has been developed on a Linux machine with Python 3.10.8. It is reco
 
 ## 🎯 Usage
 
+For a thorough explanation on how to use the package, please refer to the tutorial notebook [`protac_degradation_tutorial.ipynb`](notebooks/protac_degradation_tutorial.ipynb).
+
 After installing the package, you can use it as follows:
 
 ```python
 import protac_degradation_predictor as pdp
 
-protac_smiles = 'CC(C)(C)OC(=O)N1CCN(CC1)C2=CC(=C(C=C2)C(=O)NC3=CC(=C(C=C3)F)Cl)C(=O)NC4=CC=C(C=C4)F'
+protac_smiles = 'Cc1ncsc1-c1ccc(CNC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)COCCCCCCCCCOCC(=O)Nc2ccc(C(=O)Nc3ccc(F)cc3N)cc2)C(C)(C)C)cc1'
 e3_ligase = 'VHL'
 target_uniprot = 'P04637'
 cell_line = 'HeLa'
@@ -51,8 +50,6 @@ active_protac = pdp.is_protac_active(
     e3_ligase,
     target_uniprot,
     cell_line,
-    device='cuda', # Default to 'cpu'
-    proba_threshold=0.5, # Default value
 )
 
 print(f'The given PROTAC is: {"active" if active_protac else "inactive"}')
@@ -62,12 +59,26 @@ This example demonstrates how to predict the activity of a PROTAC molecule. The
 
 The function supports batch computation by passing lists of SMILES strings, E3 ligases, UniProt IDs, and cell lines. In this case, it returns a list of booleans indicating the activity of each PROTAC.
 
+## 📈 Training
 
 
+Before running the experiments, here are some required steps to follow (assuming one is in the repository directory already):
+1. Download the data from the [Cellosaurus database](https://web.expasy.org/cellosaurus/) and save it in the `data` directory:
+```bash
+wget https://ftp.expasy.org/databases/cellosaurus/cellosaurus.txt data/
+```
+2. Make a copy of the Uniprot embeddings to be placed in the `data` directory:
+```bash
+cp protac_degradation_predictor/data/uniprot2embedding.h5 data/
+```
+3. Create a virtual environment and install the required packages by running the following commands:
+```bash
+conda env create -f environment.yaml
+conda activate protac-degradation-predictor
+```
+4. The code for training the model can be found in the file [`run_experiments.py`](src/run_experiments.py).
 
-## 📈 Training
-
-The code for training the model can be found in the file [`run_experiments.py`](src/run_experiments.py).
+(Don't forget to adjust the `PYTHONPATH` environment variable to include the repository directory: `export PYTHONPATH=$PYTHONPATH:/path/to/PROTAC-Degradation-Predictor`)
 
 ## 📄 Citation