A comprehensive AI-powered drug discovery platform integrating 20+ AI models for molecular design, protein analysis, and biomedical research. Built with modern web technologies and state-of-the-art bioinformatics tools for researchers and computational biologists.

• Protein Analysis
• Molecular Docking
• Drug Design
• ADMET Prediction
• Chemical Databases
• Synthesis Planning
• Literature Mining

• Frontend: Next.js 14.2+ with React 18, TypeScript 5.9+, Tailwind CSS, and interactive molecular visualization
• Backend: Serverless API Routes with Supabase PostgreSQL, Redis caching, and FastAPI orchestration
• CLI + TUI: PyPI package for interfacing with the platform from a terminal, parity with webapp and allows scripting through REST API endpoint
• Storage: DigitalOcean Spaces (S3-compatible) for scalable file storage with presigned URLs
• Authentication: Clerk with multi-factor authentication and role-based access control
• Monitoring: Vercel Analytics, PostHog product analytics, and comprehensive error tracking

• Real-time Job Monitoring: WebSocket connections with progress tracking and status updates
• Atomic S3 Architecture: Centralized file storage with detailed error reporting
• Progressive Enhancement: Core functionality works without JavaScript
• Edge Computing: Vercel Edge Runtime for ultra-low latency middleware and auth checks
• Distributed Job Processing: FastAPI with Redis-backed queues and retry mechanisms

• Server-Side Rendering (SSR) for initial page loads and SEO
• Static Generation (SSG) for documentation and marketing pages
• CSRF Protection with middleware for state-changing requests
• XSS Prevention with input sanitization and Content Security Policy
• Rate Limiting with Upstash Rate Limit across API endpoints
• Row-Level Security (RLS) policies in Supabase for data isolation
• Audit Logging for security events and user actions

• End-to-End Drug Discovery: From target identification to synthesis planning
• AI-Guided Analysis: Automated literature review and hypothesis generation
• Multi-Parameter Optimization: Simultaneous optimization of efficacy, safety, and synthesizability
• Fragment-Based Design: Comprehensive fragment libraries and growing algorithms
• Safety Assessment: Comprehensive toxicity and ADMET profiling

• ivybiosciences-next - Next.js application for bioinformatics
• ivybiosciences-modal - Modal orchestration system
• ivybloom-cli - Command-line interface for biological data

• Full Documentation: docs.ivybiosciences.com
• GitHub Repository: ivybiosciences-next
• API Reference: See docs/api/ for comprehensive endpoint documentation
• Contributing: See Contributing Guidelines

We welcome contributions! Please see our Contributing Guidelines for details on:

• Code style and standards
• Pull request process
• Development workflow
• Testing requirements

• 🔐 Clerk JWT Authentication - Multi-factor authentication support
• 🔒 Row-Level Security (RLS) - Data isolation at database level
• 🚫 CSRF Protection - Token validation for state changes
• ✋ XSS Prevention - Input sanitization & Content Security Policy
• 🔑 Secret Management - Modal Secrets for secure credential handling
• 📋 Audit Logging - Comprehensive activity tracking
• 🔒 End-to-End Encryption - For sensitive data in transit

• GitHub Issues: Report bugs
• GitHub Discussions: Community forum
• Email Support: support@ivybiosciences.com
• Documentation: ivybiosciences.com/docs