Where science meets usability, clarity, and speed

NeuroCore set out to accelerate drug development for Alzheimer’s, Parkinson’s, and ALS by combining AI models with multi-omics data at scale. But their scientific teams were working in silos, relying on Jupyter notebooks, spreadsheets, and static reports to interpret findings — resulting in delays, misalignment, and missed insights.They needed more than just a dashboard. They required an interactive decision-making tool that would unify their workflows, empower both data scientists and biologists, and support rapid iteration in therapeutic discovery.Scalability partnered with NeuroCore to design a flexible, AI-integrated interface that enables seamless exploration of high-throughput model results — helping teams identify and prioritize targets faster, together.

What we did

Disconnected workflows were slowing down even the most powerful AI models.Despite building state-of-the-art algorithms for analyzing RNA-seq, proteomics, and CRISPR screens, NeuroCore’s internal teams struggled to apply those insights in real time. Biologists and data scientists were working in parallel but disconnected tracks — relying on Jupyter notebooks, static exports, and Slack threads to share experimental results.Without a centralized, intuitive interface, it was difficult to:
- Explore or compare model outputs across diseases or populations
- Prioritize drug targets based on scientific context and confidence scores
- Align decisions between research, data, and leadership teams
- Scale workflows to support multi-lab collaboration or external partnerships
NeuroCore needed a single source of truth -  a platform where insights were accessible, interpretable, and actionable across disciplines.

Scalability designed and delivered a custom interface that brought NeuroCore’s AI models to life, turning raw data into visual, explorable insight.
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We collaborated closely with NeuroCore’s translational biology and data science teams to understand their end-to-end workflows. The result was a fully integrated Target Discovery Dashboard that empowers scientists to interpret model outputs, compare molecular candidates, and accelerate decisions - all within one cohesive environment.
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Key features included:
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- A React-based interface tailored for high-throughput molecular data
- Backend integration with Python-based models and AWS-hosted data pipelines
- Interactive hit lists sortable by model confidence, pathway novelty, and druggability
- Embedded D3.js visualizations including volcano plots, pathway maps, and heatmaps
- Side-by-side comparison views to evaluate candidates across disease areas or model versions
- Role-based access for internal researchers, data scientists, and external collaborators
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By unifying exploration, comparison, and decision-making into a single interface, NeuroCore’s team gained clarity and speed - without sacrificing scientific depth.

From days to hours — and silos to sync.

What once took days of back-and-forth between teams now happens in a single session. By embedding AI results directly into a shared workspace, NeuroCore dramatically reduced the time it takes to review experiments, align on priorities, and move forward with confidence.

Since launch, the Target Discovery Dashboard has become a central part of NeuroCore’s R&D operations — supporting weekly decision-making meetings, cross-functional reviews, and even partner grant proposals.

The platform is now actively used across multiple therapeutic pipelines, with infrastructure in place to support future integration of in vivo assay data and external collaborations.

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