RNA Research (eCOMPASS platform) is an AI-powered, lab-in-the-loop RNA therapeutic development system designed to accelerate drug discovery through integrated design, experimentation, and sequencing-based analysis. The platform enables biotech researchers to design RNA sequences using AI models, run experimental validation workflows, and analyze RNA structure, expression, and binding behavior across multiple therapeutic modalities. It acts as a unified ecosystem combining AI-driven RNA design, sequencing-based biological analytics, and iterative experimental feedback loops.
From researcher login to RNA design, sequencing analytics, dataset exploration, and sample library management all in one platform
Login / Authentication system for researchers
Central dashboard for project and module navigation
eMERGE sequencing analytics and RNA insights module
RNA Library management system
Project Goal — A Closed-Loop Intelligent System for RNA Therapeutic Development
The goal of the eCOMPASS platform was to centralize RNA therapeutic development into a single intelligent system that connects AI-based RNA sequence design, laboratory experimental validation, and sequencing-based biological analysis into one continuous feedback loop. By eliminating fragmented tools scattered across disconnected research environments, the platform enables biotech teams to iterate faster and make higher-confidence decisions at every stage of drug discovery. It was engineered as a complete lab-in-the-loop ecosystem where computational predictions and real experimental outcomes reinforce each other in a structured, scalable pipeline.
Overcoming the Challenge — Fragmented Tools, Broken Feedback Loops, and Slow Iteration Cycles
Traditional RNA research workflows were fragmented across multiple disconnected tools, creating slow iteration cycles with no integrated biological feedback and heavy reliance on manual interpretation of sequencing data. Researchers struggled to connect computational predictions with real experimental outcomes in a structured way, limiting scalability for AI-driven drug design. The absence of a unified system meant each experimental cycle required significant manual coordination between computational biology and wet lab teams, compounding delays and reducing research accuracy at every stage.
👏🏽Transformative Solution — Lab-in-the-Loop Architecture for Continuous RNA Optimization
RNA Research's eCOMPASS platform introduces a lab-in-the-loop architecture where AI generates optimized RNA sequences, experimental systems validate results, sequencing platforms capture nucleotide-level data, and feedback loops automatically refine next-generation designs. Built with a modular React and TypeScript frontend powered by a GraphQL API-driven data layer, the platform creates a continuous improvement cycle connecting computational biology and laboratory operations in a single scalable ecosystem. This architecture enables faster and more accurate drug discovery pipelines by ensuring every experimental result directly informs the next round of AI-driven sequence optimization.
The Outcome
RNA Research’s eCOMPASS platform unified the entire RNA therapeutic development workflow into a single intelligent system, eliminating manual coordination between computational and laboratory teams and measurably reducing RNA candidate evaluation cycles. The platform delivered deeper biological insights at nucleotide resolution, improved sequence optimization accuracy through closed-loop experimental feedback, and reduced dependency on manual lab-data interpretation across every research stage. Biotech teams gained a scalable, centralized foundation for AI-driven drug discovery that strengthens the connection between computational predictions and real experimental outcomes.
FASTER DESIGN CYCLES
INCREASED EXPERIMENTAL THROUGHPUT
IMPROVED SEQUENCING ACCURACY
UNIFIED RESEARCH COLLABORATION
Tech Stack
Some technologies used for this project
React
TypeScript
GraphQL
AI/ML Models
