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Applied ResearchDatahubCNS Drug Development

Weeks-to-Insight Opportunity Mappingfrom Datahub AI Platform

How Negev Labs uncovered new therapeutic opportunities for a promising CNS compound in under 4 weeks using Brainify.AI's Datahub.

The Question

Negev Labs' molecule showed potential beyond its original indication. The question was:

"What other symptom domains could this mechanism address—based on how similar drugs are used in real-world practice?"

Traditionally, this means commissioning systematic literature reviews, accessing multiple data vendors, and running multi-site EHR studies — processes that take 12–16 months and require seven-figure budgets. Datahub collapsed that cycle into weeks, without sacrificing scientific rigor.

What We Used

Datahub AI Platform

Brainify.AI's end-to-end AI data platform that integrates modeling, data management, visualization, and multimodal brain dataset.

Brainify.AI Dataset

200 million medical notes connected with brain activity signals (EEG), ECG, vitals, and laboratory data in a unified, AI-ready structure.

Analog Mapping Framework

Identifies patterns of real-world use across therapies with related biological mechanisms, including their off-label utilization.

What We Did

1

Analog Mapping

Using analog mapping, Datahub isolated therapies with similar mechanisms to Negev Labs' compound and extracted their off-label real-world use.

2

Multimodal Analysis

The platform's AI models processed both structured and unstructured data — brain-activity signals, notes, and labs — to detect symptom clusters.

3

Opportunity Prioritization

Results were synthesized into a ranked map of the most promising symptoms.

The Value to Negev Labs

< 4 weeks

Speed

Delivered decision-ready insights in under four weeks, versus 12–16 months through conventional research.

$250-400k saved

Cost Efficiency

Saved an estimated $250k–$400k in combined analyst labor, data-access fees, and external vendor costs.

Data-driven

Scientific Clarity

Provided data-driven hypotheses on symptom domains guiding R&D prioritization.

Why It Worked

Unified Scale

200M

medical notes provide longitudinal clinical context alongside brain activity and clinical outcomes, eliminating data-sourcing bottlenecks.

AI-driven analytics

Proprietary models decode brain signals alongside text and labs, revealing non-obvious therapeutic correlations.

Iterative workflow

Real-time analytics allowed Negev Labs to test hypotheses, refine focus, and receive ranked results in a single sprint.

Built for translational discovery

Datahub's analytics align directly with neurobiological mechanisms, bridging exploratory data and experimental validation.

Industry Baseline Comparison

Approach
Duration
Cost
Key Limitation
Systematic literature review
~67 weeks (≈ 15 months)
≈ $280k
Fragmented, retrospective
Multi-site EHR/claims study
2–10 months (+ 2–4 months IRB)
≈ $100k–$250k
Governance delays & siloed data
Datahub AI Sprint
< 4 weeks
< $100k equivalent
Integrated, AI-driven analysis

From Hypothesis to Prioritized Insights

Negev Labs' collaboration with Brainify.AI demonstrates how AI-native data infrastructure transforms exploratory neuroscience R&D. With the Datahub platform — connecting population-scale brain data with 200 million medical notes — biotech teams can move from hypothesis to mechanism-ready insights in weeks, not years.