---
title: Drug Discovery Pipeline
emoji: 🐠
colorFrom: purple
colorTo: green
sdk: docker
pinned: false
license: mit
short_description: AI-Powered Drug Discovery Pipeline Demo
---
# 🔬 AI-Powered Drug Discovery Pipeline

<div align="center">

[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue?style=for-the-badge)](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg?style=for-the-badge)](https://opensource.org/licenses/MIT)
[![Python](https://img.shields.io/badge/python-3.8+-blue.svg?style=for-the-badge&logo=python&logoColor=white)](https://www.python.org/)
[![Docker](https://img.shields.io/badge/docker-%230db7ed.svg?style=for-the-badge&logo=docker&logoColor=white)](https://www.docker.com/)

**An interactive demonstration of how artificial intelligence and computational tools can accelerate the drug discovery process from target identification to post-market surveillance.**

[🚀 **Try Live Demo**](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline) • [📖 **Documentation**](#-overview) • [🛠️ **Installation**](#-installation--usage) • [🤝 **Contribute**](#-contributing)

</div>

---

## 🎯 Overview

This comprehensive application integrates the four major phases of pharmaceutical drug development into a single, interactive web interface. Built with cutting-edge AI and computational biology tools, it demonstrates how modern technology can accelerate and optimize the traditionally lengthy drug discovery process.

### 🔄 Pipeline Phases

<table>
<tr>
<td width="25%" align="center">

**🎯 Phase 1**
<br>
**Discovery & Target ID**
<br>
<sub>Protein analysis & compound screening</sub>

</td>
<td width="25%" align="center">

**🧪 Phase 2**
<br>
**Lead Generation**
<br>
<sub>Virtual screening & ADMET prediction</sub>

</td>
<td width="25%" align="center">

**🔬 Phase 3**
<br>
**Preclinical Development**
<br>
<sub>Molecular analysis & toxicity testing</sub>

</td>
<td width="25%" align="center">

**📋 Phase 4**
<br>
**Implementation**
<br>
<sub>Regulatory docs & pharmacovigilance</sub>

</td>
</tr>
</table>

---

## ✨ Key Features

### 🎯 **Phase 1: Discovery & Target Identification**
- **🧬 Protein Structure Fetching** - Retrieve 3D structures from PDB database
- **🔍 FASTA Sequence Analysis** - Fetch and analyze protein sequences from NCBI
- **📊 Interactive 3D Visualization** - Explore protein structures with py3Dmol
- **⚗️ Molecular Property Calculation** - Compute physicochemical properties using RDKit
- **📈 Drug-Likeness Assessment** - Evaluate compounds using Lipinski's Rule of Five
- **📊 Properties Dashboard** - Visualize molecular properties with interactive plots

### 🧪 **Phase 2: Lead Generation & Optimization**
- **🎯 Virtual Screening Simulation** - Rank compounds by predicted binding affinity
- **💊 ADMET Prediction** - Assess Absorption, Distribution, Metabolism, Excretion, and Toxicity
- **🔬 2D/3D Molecular Visualization** - Interactive molecule viewers with dark theme
- **🔗 Protein-Ligand Interaction** - Visualize binding sites and molecular interactions
- **📋 Lead Compound Analysis** - Analyze drugs like Oseltamivir, Zanamivir, Aspirin, and Ibuprofen

### 🔬 **Phase 3: Preclinical Development**
- **📊 Comprehensive Property Analysis** - Extended molecular descriptor calculations
- **🤖 AI-Powered Toxicity Prediction** - Machine learning model for toxicity risk assessment
- **🧬 Advanced Compound Profiling** - Analysis of clinical candidates including Remdesivir and Penicillin G
- **🎨 3D Molecular Gallery** - Interactive visualization of compound libraries

### 📋 **Phase 4: Implementation & Post-Market**
- **📄 Regulatory Documentation** - AI/ML model documentation templates for FDA submission
- **⚠️ Pharmacovigilance Simulation** - Real-world data analysis for adverse event detection
- **🛡️ Ethical Framework** - Guidelines for responsible AI in healthcare
- **📈 Adverse Event Analysis** - Statistical analysis and visualization of safety data

---

## 🛠️ Technical Stack

<div align="center">

### **Core Technologies**

| Category | Technologies |
|----------|-------------|
| **🖥️ Framework** | ![Streamlit](https://img.shields.io/badge/Streamlit-FF4B4B?style=flat-square&logo=streamlit&logoColor=white) |
| **🧪 Cheminformatics** | ![RDKit](https://img.shields.io/badge/RDKit-2E8B57?style=flat-square) |
| **🧬 Bioinformatics** | ![BioPython](https://img.shields.io/badge/BioPython-4169E1?style=flat-square) |
| **🎨 Visualization** | ![py3Dmol](https://img.shields.io/badge/py3Dmol-FF6347?style=flat-square) ![Matplotlib](https://img.shields.io/badge/Matplotlib-11557c?style=flat-square) |
| **🤖 Machine Learning** | ![Scikit-learn](https://img.shields.io/badge/scikit--learn-F7931E?style=flat-square&logo=scikit-learn&logoColor=white) |

### **Data Sources**

| Source | Description |
|--------|-------------|
| **🏛️ PDB** | Protein Data Bank - 3D protein structures |
| **🧬 NCBI** | Protein sequences and biological data |
| **💊 ChEMBL** | Bioactivity database (referenced) |

</div>

---

## 🚀 Installation & Usage

### 🌐 **Quick Start - Hugging Face Spaces**

The easiest way to explore the pipeline:

```bash
🔗 https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline
```

> **No installation required!** Simply click the link above to start exploring.

### 💻 **Local Development**

#### **Prerequisites**
- Python 3.8 or higher
- Git

#### **Setup**

```bash
# 📥 Clone the repository
git clone <repository-url>
cd drug-discovery-pipeline

# 🔧 Create virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# 📦 Install dependencies
pip install -r requirements.txt

# 🚀 Launch the application
streamlit run app.py
```

#### **Access the Application**
```
🌐 Local URL: http://localhost:8501
```

### 🐳 **Docker Deployment**

#### **Option 1: Quick Run**
```bash
# 🏃‍♂️ Run directly from Docker Hub (if available)
docker run -p 8501:8501 alidenewade/drug-discovery-pipeline
```

#### **Option 2: Build from Source**
```bash
# 🔨 Build the Docker image
docker build -t drug-discovery-pipeline .

# 🚀 Run the container
docker run -p 8501:8501 drug-discovery-pipeline
```

#### **Docker Compose (Advanced)**
```yaml
# docker-compose.yml
version: '3.8'
services:
  drug-discovery:
    build: .
    ports:
      - "8501:8501"
    environment:
      - STREAMLIT_SERVER_PORT=8501
    volumes:
      - ./data:/app/data  # Optional: for persistent data
```

```bash
# 🐳 Deploy with Docker Compose
docker-compose up -d
```

---

## 📋 Dependencies

<details>
<summary><strong>📦 Click to view complete requirements.txt</strong></summary>

```txt
# 🖥️ Web Framework
streamlit>=1.28.0

# 📊 Data Processing
pandas>=1.5.0
numpy>=1.24.0

# 📈 Visualization
matplotlib>=3.6.0
seaborn>=0.12.0
plotly>=5.15.0

# 🌐 Network & APIs
requests>=2.28.0

# 🖼️ Image Processing
pillow>=9.5.0

# 🧪 Cheminformatics
rdkit>=2023.3.1

# 🧬 Bioinformatics
biopython>=1.81

# 🤖 Machine Learning
scikit-learn>=1.3.0

# 🎨 3D Molecular Visualization
py3dmol>=2.0.0

# 🔧 Utilities
streamlit-option-menu>=0.3.6
streamlit-aggrid>=0.3.4
```

</details>

---

## 🎯 Use Cases & Applications

<div align="center">

| 🎓 **Educational** | 🔬 **Research** | 🏭 **Industry** |
|-------------------|-----------------|------------------|
| Drug discovery training | Proof of concept demos | Pipeline optimization |
| Cheminformatics education | Method validation | AI strategy planning |
| Bioinformatics learning | Collaborative research | Regulatory compliance |
| AI in healthcare | Publication support | Risk assessment |

</div>

### 📚 **Educational Applications**
- **🎓 University Courses** - Pharmaceutical sciences, computational biology
- **👩‍🏫 Training Programs** - Professional development in drug discovery
- **📖 Self-Learning** - Interactive exploration of drug development concepts
- **🎯 Workshops** - Hands-on demonstrations for conferences and seminars

### 🔬 **Research Applications**
- **💡 Hypothesis Generation** - Explore structure-activity relationships
- **🧪 Method Development** - Test computational approaches
- **📊 Data Visualization** - Create publication-ready figures
- **🤝 Collaboration** - Share analyses with research teams

---

## 🔬 Scientific Methodology

### **🧬 Molecular Analysis Framework**

| Method | Description | Implementation |
|--------|-------------|----------------|
| **📏 Lipinski's Rule of Five** | Drug-likeness assessment | RDKit molecular descriptors |
| **💊 ADMET Profiling** | Pharmacokinetic predictions | Machine learning models |
| **⚠️ Toxicity Modeling** | Safety risk assessment | Ensemble ML algorithms |
| **🔗 SAR Analysis** | Structure-activity relationships | Statistical correlation analysis |

### **📊 Data Integration Pipeline**

```mermaid
graph LR
    A[🧬 Structural Data] --> D[🔄 Integration Engine]
    B[📊 Chemical Data] --> D
    C[📈 Biological Data] --> D
    D --> E[🤖 AI Analysis]
    E --> F[📋 Results Dashboard]
```

---

## ⚠️ Important Disclaimers

<div align="center">

> **🚨 FOR EDUCATIONAL AND RESEARCH PURPOSES ONLY**

</div>

| ⚠️ **Limitation** | 📝 **Details** |
|-------------------|----------------|
| **🎓 Educational Tool** | Demonstration purposes only, not for actual drug development |
| **🎲 Simulated Data** | Some analyses use simulated data for illustration |
| **📋 Regulatory Compliance** | Consult regulatory agencies for actual submissions |
| **👨‍⚕️ Professional Use** | Real development requires validated, regulated systems |
| **🔬 Research Grade** | Requires validation for production use |

---

## 🤝 Contributing

We welcome contributions from the community! Here's how you can help:

### **🛠️ Development Guidelines**

```bash
# 🍴 Fork the repository
git fork https://github.com/username/drug-discovery-pipeline

# 🌿 Create a feature branch
git checkout -b feature/amazing-feature

# 💻 Make your changes
# ... code changes ...

# ✅ Test your changes
python -m pytest tests/

# 📝 Commit your changes
git commit -m "Add amazing feature"

# 🚀 Push to your branch
git push origin feature/amazing-feature

# 🔄 Create a Pull Request
```

### **📋 Contribution Areas**

- **🐛 Bug Fixes** - Fix issues and improve stability
- **✨ New Features** - Add new analysis methods or visualizations
- **📚 Documentation** - Improve README, add tutorials
- **🧪 Testing** - Expand test coverage
- **🎨 UI/UX** - Enhance user interface and experience
- **⚡ Performance** - Optimize for speed and memory usage

### **📏 Code Standards**

- **🐍 Python Style** - Follow PEP 8 guidelines
- **📝 Documentation** - Add docstrings and comments
- **🧪 Testing** - Include unit tests for new features
- **🔧 Type Hints** - Use type annotations where applicable

---

## 📞 Support & Community

<div align="center">

### **💬 Get Help**

[![Hugging Face Discussions](https://img.shields.io/badge/🤗%20Discussions-Join%20Community-yellow?style=for-the-badge)](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline/discussions)

</div>

| 🆘 **Issue Type** | 🔗 **Where to Go** |
|------------------|-------------------|
| **🐛 Bug Reports** | GitHub Issues (if available) |
| **💡 Feature Requests** | Hugging Face Discussions |
| **❓ Usage Questions** | Community Tab on HF Space |
| **📚 Documentation** | README and inline help |

---

## 📄 License & Citation

### **📜 License**
This project is licensed under the **MIT License** - see the LICENSE file for details.

### **📖 Citation**
If you use this tool in your research or education, please cite:

```bibtex
@software{drug_discovery_pipeline_2024,
  title={AI-Powered Drug Discovery Pipeline},
  author={alidenewade},
  year={2024},
  url={https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline},
  note={Interactive demonstration of AI in pharmaceutical development}
}
```

---

## 🙏 Acknowledgments

<div align="center">

**Built with ❤️ by the open-source community**

</div>

| 🏛️ **Organization** | 🎯 **Contribution** |
|---------------------|---------------------|
| **🧪 RDKit Community** | Excellent cheminformatics tools and algorithms |
| **🏛️ PDB & NCBI** | Open access to biological and structural data |
| **🖥️ Streamlit Team** | Intuitive web application framework |
| **🧬 BioPython** | Comprehensive biological computation tools |
| **🤖 Scikit-learn** | Machine learning algorithms and utilities |
| **🎨 py3Dmol** | Beautiful 3D molecular visualization |
| **🔬 Scientific Community** | Advancing computational drug discovery |

---

## 🔗 Quick Links

<div align="center">

| 🚀 **Action** | 🔗 **Link** |
|---------------|-------------|
| **🌐 Live Demo** | [Try Now](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline) |
| **👤 Author Profile** | [alidenewade](https://huggingface.co/alidenewade) |
| **🔬 ORCID** | [0009-0007-0069-4646](https://orcid.org/0009-0007-0069-4646) |
| **📚 ResearchGate** | [Ali Denewade](https://www.researchgate.net/profile/Ali-Denewade) |
| **💬 Discussions** | [Community](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline/discussions) |
| **📊 Analytics** | [Space Stats](https://huggingface.co/spaces/alidenewade/drug-discovery-pipeline) |

---

<sub>⭐ **Star this project if you find it useful!** ⭐</sub>

</div>