Brainify.AI today announced the results of a rapid discovery sprint with Negev Labs, a biotech company exploring new therapeutic applications for a promising CNS compound. Leveraging Brainify.AI’s Datahub — an AI-native multimodal brain data platform built on 2.7 million patient records — the joint team identified high-value symptom domains for mechanism-driven follow-up studies in less than four weeks, a process that traditionally requires more than a year.
The collaboration showcases how Brainify.AI is redefining exploratory neuroscience R&D through unified datasets, AI-driven analytics, and an accelerated research workflow.
A New Standard for Hypothesis Generation
Negev Labs’ goal was to understand where their molecule might demonstrate therapeutic benefit beyond its initial indication. Instead of commissioning lengthy literature reviews, contracting multiple data vendors, or running multi-site EHR studies — all of which typically take 12–16 months and cost hundreds of thousands of dollars — they turned to Brainify.AI’s Datahub platform.
Datahub integrated signals from EEG, ECG, clinical notes, vitals, and laboratory data, enabling a comprehensive, mechanism-aligned investigation. Using analog mapping, the platform identified therapies with related mechanisms and explored their real-world, off-label utilization. AI models then processed structured and unstructured data to surface symptom clusters and prioritize the most promising opportunities.
Impact for Negev Labs
Speed: Decision-ready insights delivered in under four weeks compared to more than a year of conventional research.
Cost Efficiency: An estimated savings of $250,000–$400,000 across analyst labor, data-access fees, and external vendor costs.
Scientific Clarity: Data-driven hypotheses pinpointed symptom domains most aligned with the compound’s neurobiological mechanism.
Why Datahub Outperformed Traditional Approaches
On page 2 of the case study, the comparison table clearly shows the advantage: a Datahub sprint costs under $100k equivalent and bypasses the fragmented nature of literature reviews and the governance hurdles of multi-site EHR studies.
The platform’s strength comes from:
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Scale: A unified multimodal dataset covering 2.7 million patients.
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AI-Native Analytics: Proprietary models decode brain signals alongside clinical text and labs to uncover patterns missed in conventional datasets.
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Iterative Workflow: Real-time analytics allow teams to refine hypotheses and see ranked results rapidly.
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Mechanism Alignment: Datahub’s architecture is built for translational neuroscience, bridging exploratory signals with experimental validation.
Transforming the Future of CNS Drug Discovery
The Brainify.AI–Negev Labs partnership demonstrates a new paradigm for neuroscience R&D: fast, mechanism-aware, cost-efficient discovery powered by multimodal brain data. As shown in the case study, teams can now move from hypothesis to prioritized insights in weeks rather than years.
