Matterworks, a cutting-edge biotech AI company, has raised an undisclosed amount in Series A funding to scale its molecular intelligence platform. The round included participation from Lewis and Clark Partners, OMX Ventures, Pillar VC, Germin8 Ventures, Intermountain Ventures, Tarsadia, and others.

Founded by Jack (J.M.) Geremia, PhD, Matterworks is building a new class of infrastructure to extract insights from unstructured biological data - helping scientists accelerate discoveries in drug development, synthetic biology, and biomarker identification.

While many platforms focus on structured omics data or imaging, Matterworks targets the long tail of biological complexity - the poorly labeled, messy, and often-ignored molecular signals hiding in the dark matter of life sciences research.

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What Matterworks Actually Does

At the core of Matterworks is its proprietary Latent Space of Molecules (LSM) Platform - a machine learning engine that turns raw biological data into structured representations usable by chemists, biologists, and data scientists.

The platform enables teams to:

• Clean, normalize, and structure complex molecular datasets
  
• Surface correlations across omics, literature, and historical experiments
  
• Generate predictive models for new compound design or hypothesis testing
  
• Integrate seamlessly with existing lab systems and cloud-based R&D stacks
  

Matterworks provides insights that were previously too noisy, too unstructured, or too buried in edge cases for traditional analytics.

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Why This Matters for Biotech’s Next Leap

Biological research is producing exponential volumes of data, yet much of it goes unused:

• 80% of biological data remains unstructured, according to Nature Biotechnology
  
• Laboratories generate over 1 million gigabytes of new data per year globally - yet most of it lacks labeling or is trapped in proprietary formats (Deloitte)
  
• AI-driven platforms in drug discovery raised $5.4 billion in VC in 2023, but the majority still rely on pre-processed, curated datasets (PitchBook)
  
• A recent McKinsey study shows that AI applied to unstructured life sciences data could reduce time-to-discovery by up to 30% and increase R&D ROI by over 20%
  

Matterworks sits at this critical junction - where AI meets the hardest data. Their platform doesn't just make analysis possible; it makes the invisible actionable.

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Why This Raise Is a Signal

In an industry flooded with AI-for-biotech startups, Matterworks took a contrarian path: rather than chasing models that work on perfect inputs, they built tools to make imperfect inputs usable.

Instead of focusing on downstream predictions or glossy visualizations, they went upstream  -  to the chaotic, unlabeled, and unstructured molecular data that most platforms ignore. Because that’s where bottlenecks really live.

This is where smart founders zoom in. Matterworks realized that the biggest blocker in biotech wasn’t the absence of models. It was the absence of usable material to feed into them. The problem wasn’t the algorithm  -  it was the entropy of the system around it.

Here’s the real insight: if you're building in a space where everything feels stuck, the leverage often isn’t found by improving the output. It's found by fixing the inputs no one else wants to touch.

Matterworks didn’t win by predicting faster. They won by asking: What if we made everything downstream faster just by structuring what’s already here?

That’s not just product-market fit  -  that’s product-ecosystem fit. When your value becomes the enabler for everyone else's, you don't need to compete. You become the default.

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Market Outlook: Unlocking Biology’s Dark Data

Matterworks is entering a breakout phase of the data-infrastructure-for-biology movement. AI-native drug discovery can’t succeed without the infrastructure to ingest, structure, and learn from noisy, fragmented input.

Key developments include:

• BenchSci, Recursion, and Insitro have collectively raised over $2.4B to bring structure to biological experimentation
  
• Digital biology platforms are now the fastest-growing segment of bioinformatics, forecast to grow at 23.2% CAGR through 2030 (GlobalData)
  
• Mass spectrometry and other rich, high-throughput tools are generating 2–3x more data per year, yet lack cross-lab standards for integration
  
• Regulatory bodies like the FDA are now issuing guidance for AI model transparency in drug discovery, accelerating demand for traceable inputs
  

With no-code APIs, explainable AI outputs, and deep integration into molecular R&D workflows, Matterworks is creating the connective tissue that links raw data to next-generation breakthroughs.

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What’s Next for Matterworks?

With its new funding, Matterworks will:

• Expand its engineering and computational biology teams to deepen LSM platform capabilities
  
• Develop tools for multimodal data ingestion - including spectroscopy, microscopy, and proteomics pipelines
  
• Launch beta programs with mid-size biotechs and academic research hospitals
  
• Build out auditability and explainability layers for regulatory collaboration
  
• Enable plug-and-play modules for pharma IT teams to integrate directly with Matterworks APIs
  

The company also plans to open-source select components of its molecule representation models to foster ecosystem growth and accelerate scientific transparency.