Protein-Design-and-Drug-Discovery-Research-Assistant

Overview The Protein Design Research Assistant is a comprehensive web application that leverages NVIDIA's Inference Microservices (NIMs) and LlamaIndex for advanced protein design and analysis. This application is specifically designed to run in Google Colab, providing easy access to GPU resources and avoiding local dependency conflicts.

🎯 Purpose This project was developed for the LlamaIndex-NVIDIA Developer Contest to demonstrate the effective integration of NVIDIA's AI services with LlamaIndex for protein design applications. The application showcases how to: Utilize NVIDIA's Inference Microservices (NIMs) for protein structure prediction and design Implement LlamaIndex for efficient knowledge retrieval and context-aware responses Leverage Google Colab's GPU resources for optimal performance

🚀 Features

1. Literature Search: Search and analyze scientific papers from arXiv and PubMed AI-generated paper summaries Voice-enabled chat assistance Integration with NVIDIA knowledge base https://pubmed.ncbi.nlm.nih.gov/ https://arxiv.org/

2. PDB Tools: Abstract lookup for quick protein information PDB file download capabilities Interactive 3D structure visualization AI-powered structure analysis https://www.rcsb.org/

3. RFdiffusion: Generate new protein structures GPU-accelerated protein backbone generation Custom binder design capabilities Parameter optimization with AI assistance https://build.nvidia.com/ipd/rfdiffusion

4. ProteinMPNN: Optimize amino acid sequences Deep learning-based sequence prediction Integration with NVIDIA GPU acceleration Advanced parameter configuration https://build.nvidia.com/ipd/proteinmpnn

5. AlphaFold2: Protein structure prediction NVIDIA NGC optimization Parameter customization Results analysis and visualization https://build.nvidia.com/deepmind/alphafold2

6. MolMIM: Molecular generation and optimization CMA-ES algorithm implementation Latent space exploration Property optimization https://build.nvidia.com/nvidia/molmim-generate

7. DiffDock: Molecular docking via diffusion models Score-based pose generation Confidence model ranking GPU-accelerated calculations https://build.nvidia.com/mit/diffdock

8. GROMACS: Molecular dynamics simulation protocols NVIDIA GPU optimization DeepSeek-enhanced protocol generation Performance analysis https://catalog.ngc.nvidia.com/orgs/hpc/containers/gromacs

📋 Prerequisites

API Keys Required

NVIDIA API Key (Required) Sign up at NVIDIA Developer Portal Generate API key for accessing NIMs

https://build.nvidia.com/deepseek-ai/deepseek-coder-6_7b-instruct

OpenAI API Key (Required) For GPT-4 integration and voice synthesis

https://platform.openai.com/docs/overview

Pinecone API Key (Required) For vector database functionality

https://www.pinecone.io/

Phoenix API Key (Required) For enhanced monitoring and tracing

https://phoenix.arize.com/

Google Colab Requirements A Google account Access to Google Colab Sufficient Colab credits for GPU usage

https://colab.research.google.com/

🛠️ Installation

Open the Google Colab notebook Run the initial setup cell to install dependencies:

Configure your environment variables:

import os

os.environ["NVIDIA_API_KEY"] = "your_key_here"

os.environ["OPENAI_API_KEY"] = "your_key_here"

os.environ["PINECONE_API_KEY"] = "your_key_here"

os.environ["PHOENIX_API_KEY"] = "your_key_here"

💻 Usage Starting the Application Run all cells in the Colab notebook Click the generated Gradio link to access the web interface Navigate through different tabs for specific functionalities Example Workflows

Protein Structure Generation

Example using RFdiffusion

1. Upload PDB file
2. Set contig string
3. Specify hotspot residues
4. Adjust diffusion steps
5. Generate and run script

Sequence Optimization

Example using ProteinMPNN

1. Upload structure
2. Configure parameters
3. Generate sequences
4. Analyze results 🔍 Technical Details

Architecture Frontend: Gradio web interface

Backend: Python with multiple AI services

Database: Pinecone vector store

AI Models: NVIDIA NIMs integration

Monitoring: Phoenix telemetry

Key Components:

LlamaIndex for knowledge retrieval

OpenAI GPT-4o for chat assistance

NVIDIA NIMs for protein design

OpenTelemetry for monitoring

⚠️ Important Notes

This application is specifically designed for Google Colab to avoid dependency conflicts and leverage cloud GPU resources Local installation is not recommended due to complex dependencies Some features may require specific GPU resources in Colab API usage may incur costs depending on your service plans

🐛 Known Issues

Dependency conflicts when running locally Specific CUDA version requirements Memory limitations in free Colab tier Timeout issues with long-running processes

📚 Resources

NVIDIA NIMs Documentation LlamaIndex Documentation Google Colab Documentation Gradio Documentation

🤝 Contributing

This project is currently part of the LlamaIndex-NVIDIA Developer Contest and is not accepting external contributions.

📄 License

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

👥 Authors

Joseph Steward - Initial work - GitHub

🙏 Acknowledgments NVIDIA for providing NIMs and GPU resources LlamaIndex team for vector store capabilities Google Colab for cloud computing resources OpenAI for language model support

📧 Contact For questions and support, please contact [email protected]

Note: This project is a submission for the LlamaIndex-NVIDIA Developer Contest and is optimized for Google Colab usage. Local installation is not supported.