🔬 Drug-Target Binding Affinity Visualizer

This is a Streamlit-based interactive application for visualizing drug–protein binding interactions using real-world bioinformatics data from the BindingDB database.
It analyzes binding affinity scores (Ki values) and presents clear, interactive visualizations to help researchers explore strong drug–target candidates.

🌐 Live App: Click here to try the Streamlit App

---

📊 Project Overview

• Goal: Analyze and visualize ligand–target interactions using Ki values
• Data Source: BindingDB
• Key Technologies: Python, Pandas, NumPy, Streamlit, Matplotlib, Seaborn, Plotly

---

🛠 Workflow

Steps:

1. Dataset – BindingDB CSV file containing target names, ligand SMILES, and Ki values
2. Data Processing – Cleaning, preprocessing, and formatting
3. Binding Affinity Scoring – Converting Ki values into custom interaction scores
4. Visualization – Bar charts, histograms, swarm plots, and 2D/3D scatter plots
5. Streamlit App – Upload your dataset and explore interactively

---

🚀 Features

• 📥 Upload your own BindingDB CSV dataset for instant analysis
• 📊 Visualize top ligands and their binding targets by interaction score
• 🧠 Binding strength scoring system for quick interpretation
• 📈 Multiple visualizations – bar charts, histograms, swarm plots, and 2D/3D scatter plots
• 💾 Download sample CSV to test the app instantly
• 🧬 Easy to extend for machine learning–based drug discovery

---

⚗️ Binding Affinity Scoring System

Ki (nM) Range	Score	Interpretation
Ki < 10	100	Very Strong Binding
10 ≤ Ki < 100	70	Strong Binding
100 ≤ Ki < 1000	40	Moderate Binding
Ki ≥ 1000	10	Weak Binding

Note: Lower Ki = Higher Binding Affinity
This scoring system helps identify potent drug candidates in drug discovery pipelines.

---

💡 How to Run Locally

1. Clone the Repository

   git clone https://github.com/ShubhamBioIT/Protein-Drug-affinity-visualizer.git
   cd Protein-Drug-affinity-visualizer
   

install dependencies & Run App

pip install -r requirements.txt
streamlit run app.py

THANK YOU