metadata

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

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 • 📖 Documentation • 🛠️ Installation • 🤝 Contribute

🎯 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

🎯 Phase 1
Discovery & Target ID
_{Protein analysis & compound screening}

🧪 Phase 2
Lead Generation
_{Virtual screening & ADMET prediction}

🔬 Phase 3
Preclinical Development
_{Molecular analysis & toxicity testing}

📋 Phase 4
Implementation
_{Regulatory docs & pharmacovigilance}

✨ 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

Core Technologies

Category	Technologies
🖥️ Framework
🧪 Cheminformatics
🧬 Bioinformatics
🎨 Visualization
🤖 Machine Learning

Data Sources

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

🚀 Installation & Usage

🌐 Quick Start - Hugging Face Spaces

The easiest way to explore the pipeline:

🔗 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

# 📥 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

# 🏃‍♂️ Run directly from Docker Hub (if available)
docker run -p 8501:8501 alidenewade/drug-discovery-pipeline

Option 2: Build from Source

# 🔨 Build the Docker image
docker build -t drug-discovery-pipeline .

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

Docker Compose (Advanced)

# 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

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

📋 Dependencies

📦 Click to view complete requirements.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

🎯 Use Cases & Applications

🎓 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

📚 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

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

🚨 FOR EDUCATIONAL AND RESEARCH PURPOSES ONLY

⚠️ 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

# 🍴 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

💬 Get Help

🆘 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:

@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

Built with ❤️ by the open-source community

🏛️ 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

🚀 Action	🔗 Link
🌐 Live Demo	Try Now
👤 Author Profile	alidenewade
🔬 ORCID	0009-0007-0069-4646
📚 ResearchGate	Ali Denewade
💬 Discussions	Community
📊 Analytics	Space Stats

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