Episode 63: Enveda Biosciences

In this episode of Petri Dish Perspectives, we explore the story of Enveda Biosciences, one of the most exciting AI-native biotech companies redefining how drugs are discovered. While most pharmaceutical companies build vast synthetic chemical libraries, Enveda is using artificial intelligence, metabolomics, and mass spectrometry to unlock the world's largest and largely untapped chemical library: nature.

We'll trace the history of natural product drug discovery, from penicillin and paclitaxel to the AI revolution, dive into founder Viswa Colluru's vision of creating a searchable map of nature's chemistry, and explore how Enveda's platform is transforming plants, fungi, and microbes into tomorrow's medicines. We'll also discuss the company's growing clinical pipeline, why investors have poured hundreds of millions into the platform, and whether AI can finally solve one of drug discovery's oldest challenges.

Because sometimes, the future of medicine isn't invented, it evolves.

🎧 Listen now, stay curious, and don’t forget to subscribe for new episodes every Thursday!

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Transcript

Hello and welcome to Petri Dish Perspectives, the podcast where we geek out about science and the companies shaping the future of healthcare. I’m your host, Manead, and I’m a PhD scientist by training, biotech storyteller by choice. With every new episode released on Thursday, my goal is to deliver digestible pieces of information on healthcare companies under 30 mins. 

If you were asked where the next blockbuster drug might come from, you might think of a gleaming pharmaceutical research campus, a CRISPR laboratory, or perhaps an AI company training enormous protein models. Few people would answer, "the leaves of a rainforest shrub" or "a fungus growing on a mountain in Peru." Yet nearly half of all approved medicines can trace their origins back to natural products or molecules inspired by nature. Penicillin came from mold. Paclitaxel came from the Pacific yew tree. Artemisinin came from sweet wormwood. Nature has been running the world's largest medicinal chemistry experiment for billions of years.

The problem isn't that nature lacks medicines. The problem is that we've barely explored it.

Scientists estimate there are more than 400,000 known plant species and millions of fungi, bacteria, and marine organisms. Each produces thousands of unique molecules as part of its own evolutionary survival strategy. Collectively, this represents one of the largest chemical libraries on Earth—far larger than anything humans have ever synthesized. Yet traditional natural products discovery has been painfully slow, unpredictable, and expensive. Researchers often spend years isolating compounds only to rediscover molecules that were already known.

Enveda Biosciences believes artificial intelligence can fundamentally change that equation.

Just in September of 2025, Enveda raised $150 million, bringing its total funding to $517 million, at a valuation of more than $1 billion. The round was led by Premji Invest, the family office of Indian billionaire Azim Premji, founder of Wipro.

Quick disclaimer, I give full credit to the original articles cited in the references in the transcript!

Grab a coffee or tea, settle in, and let’s jump in!

The Origins: Rediscovering Natural Products

Natural products have always occupied a fascinating place in pharmaceutical history.

Before synthetic chemistry became dominant after World War II, nearly every medicine originated from plants, fungi, or microorganisms. Aspirin evolved from willow bark. Morphine from poppies. Quinine from cinchona bark. Digoxin from foxglove. Even today's oncology arsenal owes much to compounds discovered in plants and microbes.

Then something changed.

Beginning in the 1990s, pharmaceutical companies largely abandoned natural product discovery. High-throughput screening and combinatorial chemistry promised faster results. Companies believed they could simply generate millions of synthetic molecules and let automation discover the winners.

It didn't quite work.

Synthetic libraries produced enormous numbers of compounds, but relatively few breakthrough medicines. Meanwhile, nature continued evolving molecules that had already been optimized through millions of years of evolutionary selection.

Natural products weren't failing.

The tools simply weren't good enough to find them.

The Founder: Visions Beyond Traditional Drug Discovery

Enveda was founded in 2019 in Boulder, Colarado by Dr. Viswa Colluru. The company's name is a creative blend of the English word "enable" and the Sanskrit word "veda," which translates to "knowledge".

Unlike many biotech founders who spend decades climbing the academic ladder before starting a company, Viswa Colluru took a far less conventional route. Trained as an engineer with a background spanning computer science, biotechnology, and entrepreneurship, he became fascinated by a deceptively simple question: if evolution has spent billions of years inventing molecules, why are humans still discovering medicines one plant at a time?

Before founding Enveda, Colluru worked across healthcare technology and life sciences startups, developing an appreciation for how artificial intelligence could transform industries that were drowning in data but starving for insight. He saw genomics undergo its sequencing revolution. He watched machine learning reshape image recognition and language processing. Yet natural product chemistry—the source of many of medicine's greatest breakthroughs—remained surprisingly analog. Scientists still traveled to remote ecosystems, collected samples, ran mass spectrometry experiments, and manually identified compounds one by one, often rediscovering molecules that had already been described decades earlier.

Colluru believed the problem wasn't that nature had run out of useful chemistry. The problem was that humans lacked the computational tools to read it. Every plant, fungus, and microorganism contains thousands of metabolites, each representing millions of years of evolutionary optimization. To him, forests weren't simply collections of plants—they were enormous biological databases whose chemical language remained largely undeciphered.

That insight became the foundation for Enveda Biosciences.

So why hadn't anyone built the equivalent platform for natural chemistry?

The challenge wasn't collecting plants.

It was interpreting millions of previously unknown molecular signatures hidden within them.

Colluru envisioned combining metabolomics, mass spectrometry, machine learning, and ethnobotanical knowledge into one integrated discovery engine.

Instead of asking whether one plant might contain one useful compound, Enveda asks whether AI can learn the language of chemistry across the entire natural world.

Building the Platform: Turning Plants into Data

The core technology behind Enveda resembles what AlphaFold did for proteins—but applied to small molecules.

Every plant contains thousands of metabolites. When scientists analyze those compounds using mass spectrometry, they obtain enormous datasets consisting of fragmentation patterns rather than easily identifiable molecular structures.

Traditionally, identifying those compounds requires painstaking laboratory work.

Enveda instead trains AI models to recognize molecular fingerprints directly from spectral data.

The company combines: High-resolution mass spectrometry… Machine learning… Computational chemistry… Natural language processing… Historical scientific literature… Ethnobotanical records...

And proprietary biological screening.

The result is what amounts to a continuously expanding chemical atlas of nature.

Instead of rediscovering molecules scientists already know, Enveda predicts which unknown compounds are both structurally novel and biologically relevant before expensive laboratory work even begins.

The company describes this as creating a Google Maps for natural chemistry.

Funding the Vision: Convincing Silicon Valley that Nature Still Matters

Building this type of platform required enormous computational resources and equally ambitious investors.

Unlike traditional biotech startups founded around a single molecule, Enveda positioned itself as a technology platform capable of repeatedly generating drug candidates.

Investors responded enthusiastically.

The company attracted backing from Lux Capital, General Catalyst, Dimension Capital, Premji Invest, Microsoft's M12 venture fund, and several leading life sciences investors.

Over successive financing rounds, Enveda raised well over half a billion dollars, making it one of the best-funded AI-native drug discovery companies outside the protein prediction space.

This funding allowed Enveda to scale globally—building field collection programs, laboratory automation, computational infrastructure, and one of the world's largest proprietary natural products datasets.

Rather than betting everything on one therapeutic program, investors were betting on an entirely new discovery engine.

The Science: Why Nature Still Beats Synthetic Libraries

One reason Enveda has attracted so much attention is because natural products remain uniquely successful in drug discovery.

Evolution optimizes molecules under real biological constraints. Plants develop chemicals to repel insects. Fungi evolve antibiotics to compete against bacteria. Marine organisms generate toxins to survive hostile ecosystems. Every one of these molecules has already undergone millions of years of biological optimization.

Synthetic chemists can create millions of compounds. Nature has already created billions.

Enveda's thesis is that AI allows humanity to finally search that chemical diversity systematically rather than randomly.

Pipeline: From Platform to Patients

Like many AI-first companies, Enveda initially faced skepticism. Could they actually translate computational predictions into real medicines?

Over the past few years, the answer has increasingly become yes. The company has advanced multiple internally discovered programs into preclinical and early clinical development, targeting inflammatory diseases, dermatology, immunology, and pain.

Rather than licensing discoveries immediately, Enveda has increasingly chosen to build its own therapeutic pipeline.

That transition mirrors companies like Alnylam and Regeneron, which evolved from platform providers into fully integrated biotechnology companies.

Its lead program, ENV-294, is an oral anti-inflammatory therapy currently in Phase 2 development for atopic dermatitis and asthma. The molecule is designed to provide efficacy comparable to biologic therapies like dupilumab while offering the convenience of a once-daily pill, positioning it as a potential "pipeline-in-a-product" with applications across multiple inflammatory diseases. 

Beyond immunology, Enveda is developing ENV-308, an oral therapy in Phase 1 for chronic weight maintenance, targeting patients after initial weight loss through a novel hormone-mimetic mechanism that is independent of GLP-1 signaling. 

The company's third clinical asset, ENV-6946, is a gut-restricted small molecule in Phase 1 for inflammatory bowel disease, designed to modulate multiple inflammatory pathways through a single novel target while avoiding systemic toxicities associated with injectable biologics. Beyond these clinical programs, Enveda has several additional IND-enabling candidates and a growing portfolio of development-stage assets across inflammation, metabolism, and other chronic diseases, reflecting its strategy of building not just individual drugs, but a repeatable AI-powered engine capable of continuously translating nature's chemical diversity into new medicines. 

Partnerships and Industry Recognition

Enveda's rise has attracted attention throughout the pharmaceutical industry.

Major pharmaceutical companies increasingly recognize that AI alone is insufficient.

Large language models require high-quality biological data.

Enveda possesses something difficult to replicate: proprietary chemical data generated directly from nature.

Rather than relying solely on public databases, the company continuously expands its own molecular knowledge graph through laboratory experimentation.

In today's AI landscape, proprietary datasets may become even more valuable than algorithms themselves.

Lessons from Enveda

Enveda offers several lessons about where biotechnology is heading.

First, biology is becoming a data science.

DNA was only the beginning. Increasingly, proteins, metabolites, chemical structures, and even ecosystems are becoming computational datasets.

Second, proprietary data matters more than algorithms alone.

Machine learning models are becoming increasingly accessible. Unique biological datasets remain difficult to replicate.

Third, platform companies eventually need products.

Investors initially reward technology platforms, but long-term value comes from medicines reaching patients.

Finally, nature remains biotechnology's greatest engineer.

Artificial intelligence isn't replacing biology.

It's helping us understand biology at a scale previously impossible.

What's Next: Building the World's Largest Chemical Atlas

Looking ahead, Enveda aims to continue expanding its molecular atlas while advancing multiple clinical candidates.

The broader vision extends beyond discovering one blockbuster drug.

Imagine an AI system capable of predicting therapeutic molecules from virtually any plant, fungus, or microorganism on Earth.

Imagine searching nature the way we search Google today.

Imagine compressing decades of natural products chemistry into weeks.

If successful, Enveda could fundamentally reshape how medicines are discovered—not by replacing nature, but by finally giving scientists the tools to understand it.

Outro: The Future Was Growing Around Us All Along

For decades, the pharmaceutical industry believed synthetic chemistry represented the future.

Enveda is making a compelling argument that perhaps the future has been growing around us all along.

The company isn't trying to outsmart evolution.

It's trying to learn from it.

By combining artificial intelligence with billions of years of natural experimentation, Enveda represents a new model of biotechnology—one where computational power meets evolutionary biology.

Whether Enveda ultimately becomes the next Regeneron or simply inspires an entirely new generation of natural-product drug discovery companies remains to be seen. But one thing is becoming increasingly clear: the next revolution in medicine may not begin with designing molecules from scratch.

It may begin by finally understanding the extraordinary chemistry that nature has been quietly perfecting for billions of years.

This has been Petri Dish Perspectives. I’m Manead. Thanks for listening. See you next Thursday. Good bye.

References

1. https://enveda.com/ 
2. www.wikipedia.org
3. https://endpoints.news/ 
4. https://finance.yahoo.com/ 

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