SynergyForge: A Discovery Engine for Longevity & Neuro-Enhancement

Live Demo: https://neuroidss.github.io/Combination-Synergy-Engine/

SynergyForge is a browser-first platform for an AI agent capable of autonomous bioinformatics research. It mines thousands of scientific papers to discover novel, synergistic interventions for longevity and powers an educational organoid simulation game to visualize their effects.

The core of the platform is a discovery engine that goes beyond simple search. It builds a real-time, financially-oriented Intervention Knowledge Space to identify unexplored connections between research areas and generate novel, testable, and cost-analyzed investment opportunities.

The Discovery Engine: Finding What's Next

Instead of mapping papers, SynergyForge builds a dynamic knowledge graph where the nodes are interventions (e.g., Metformin, Exercise) and the edges are synergies. This creates a living, financially-oriented model of the scientific landscape that directly answers the question, "Where should we invest next?"

1. Real-time Intervention Knowledge Space: As the agent reads scientific literature, it populates a "knowledge space" in real-time. Each new intervention discovered becomes a node in this space.

2. Instant Costing & Synergy Identification: For each intervention, the agent immediately estimates its in-vitro validation cost. It then identifies known synergies described in the literature, creating connections between intervention nodes. Each connection is immediately scored for scientific promise and its total cost is calculated.

3. On-the-Fly Hypothesis Generation: This is the core of the discovery engine. After analyzing an article and identifying the interventions it discusses (e.g., A, B, and C) and the synergies it proves (e.g., A+B), the AI immediately looks for "knowledge gaps." It asks: "What about A+C, or B+C?" It then generates these hypothetical synergies, scores them, costs them, and adds them to the knowledge space as new, high-potential investment opportunities.

Use Case 1: Discovering Neuronal Anti-aging Therapies

To find new ways to reverse brain aging, the engine is given the objective: "Discover synergistic interventions to reverse neuronal aging by enhancing mitochondrial function, promoting neurogenesis, and reducing neuroinflammation."

• Result: The engine analyzes a meta-review on neuronal aging, identifying interventions like "Urolithin A" (enhances mitophagy), "Lion's Mane" (promotes neurogenesis), and "transcranial magnetic stimulation (TMS)" (reduces neuroinflammation).
• Known Synergy: It extracts a known synergy between Urolithin A and Lion's Mane, immediately costing and scoring it.
• Generated Hypothesis: The AI notes that no combination with TMS is mentioned. It generates a novel, tri-modal hypothesis: combining Urolithin A and Lion's Mane with a targeted TMS protocol to create a powerful, multi-pronged approach to clear damaged components, rebuild neurons, and create a healthier environment for them to thrive in. This new, cost-analyzed proposal appears instantly in the investment opportunities feed.

Use Case 2: Engineering "Superneurons"

To explore cognitive enhancement, the objective is set to: "Identify interventions to enhance cognitive function, memory, and network efficiency beyond the physiological baseline."

• Result: The engine processes an article discussing "ampakines" (a class of cognitive enhancers) and "meditation" (a behavioral intervention).
• Hypothesis Generation: It immediately generates a hypothetical synergy, proposing that a low dose of an ampakine could amplify the plastic changes in the brain induced by focused-attention meditation, leading to a synergistic improvement in learning rates. This novel "pharma-behavioral" combination is costed, scored, and presented as a new opportunity.

Key Features

• Real-time Intervention Knowledge Space: Identifies interventions from literature, costs them, and maps both known and hypothetical synergies between them.
• Scientific Validation Engine: An AI pipeline that searches PubMed, patents, and preprints, validates primary sources, scores their reliability, and provides concise summaries.
• Synergy Analysis Engine: Analyzes validated literature to identify and score synergistic, additive, or antagonistic interactions between interventions (drugs, devices, and behaviors).
• "Organoid Odyssey" Simulator: An engaging simulation where users apply discovered combinations to virtual neural organoids and observe the effects on health and lifespan across four major theories of aging.
• Self-Improving Agent: The agent can create new tools and workflows, allowing it to learn and become more effective at research over time.
• Client-First / Serverless: The entire AI research and simulation UI runs in the browser without any required backend. Simply open index.html to get started.

Getting Started

Simply open the index.html file in a modern web browser (like Chrome or Edge). No installation or server is required to run the core application.