Evidence-driven updates across biotech and AI

Articles, whitepapers and podcasts from our team and advisors.

Podcasts
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AI, LABI, and How we Invest: A Conversation with Neel Madhukar, PhD and Themasap Khan, PhD

Neel Madhukar, PhD, Chief Technology Officer of Luma Group’s proprietary AI model, LABI, discusses the genesis of the program and how it impacts day-to-day processes at Luma Group. Neel and Themasap discuss what we are building at Luma Group to change the game for biotech investing.

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Whitepapers

God Bless the Private Sector: How Private Funding is Bridging the Gap

Publicly funded basic research has long served as the foundation for breakthroughs. Yet proposed federal cuts, including reductions to the NSF and NIH, now threaten the early-stage research that the private sector traditionally does not fund. These changes have already stalled projects, eliminated jobs, and raised concerns about broader delays in scientific progress. At the same time, the United States faces intensifying competition with China, whose sustained investment in early-stage biotech has accelerated its global share of drug development. Although the US retains leadership in total R&D value, the trend signals growing vulnerability, one seemingly at odds with the administration’s broader “America First” agenda. The launch of the Genesis Mission, with its emphasis on AI-enabled science and biotechnology as a domain of national importance, hints at a potential restructuring rather than an abandonment of federal scientific priorities. In this environment, the private sector has emerged as an essential stabilizing force. As public funding recedes, venture capital and philanthropy must intervene earlier in the research lifecycle. This shift can bring rigorous private-sector diligence to basic science, likely focusing resources on fewer projects but with greater efficiency. To handle this complexity, biology-specific AI tools will shift from optional luxuries to indispensable necessities. Ultimately, this disruption offers a chance to modernize our infrastructure: building a resilient ecosystem capable of expanding, not just restoring, American scientific progress.

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Podcasts
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Reimagining Chronic Care: A Conversation with Ritish Patnaik of Curve Biosciences

Dr. Themasap Khan, co-founder and partner, sat down with Ritish Patnaik, CEO and co-founder of Curve Biosciences, to discuss his career, the journey to co-founding Curve, the company’s transformative impact, and what’s next. Beyond delving into Curve’s groundbreaking science, Ritish shares leadership insights and advice for navigating the world of biotech, explaining how his life experience pushed him to biotech.

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Articles

The Next Frontier in Chronic Disease Monitoring: Our Investment in Curve Biosciences

Despite remarkable medical advances, real-time monitoring of organ health remains out of reach. Curve Biosciences is closing this gap with Whole-Body Intelligence™—a platform built on the world’s largest curated tissue atlas that maps how disease reshapes each organ at the molecular level. By tracing these precise signatures in blood, Curve delivers liquid biopsy tests that move beyond cancer to detect and monitor disease across the full spectrum of organ health. Its first product, a liver test, is already demonstrating superior accuracy and the potential to transform early detection and chronic disease management.

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Whitepapers

The Next Era of Care for Neurodegenerative Disease

Neurodegeneration is a gradual loss of brain function that unfolds over decades before symptoms appear. It isn’t quite a silent killer like heart disease or cancer; it’s an emotional long goodbye. While neurodegeneration manifests in many ways, severe memory loss from dementia is among the most common outcome of this irreparable brain damage. Each year, millions of individuals with dementia experience progressive losses in memory, personality, and functional independence. Countless family members and caregivers bear witness to this devastating life deterioration. Despite its toll on society, innovations combatting neurodegenerative disease have lagged far behind advances in heart disease that produced statins and the rapidly expanding arsenal of cancer-fighting drugs. These shortcomings are not due to lack of funding or interest from society, but instead because drug development for the brain faces complex challenges that other therapeutic areas do not. Alzheimer’s Disease (AD) is the most common form of neurodegeneration and is responsible for ~70% of dementia cases. Over the past two years, the AD drug development space has started to turn a corner, highlighted by the first two disease modifying drug approvals. For the first time, patients have drugs that modestly slow cognitive decline, giving hope to both the patient and drug development communities.At Luma Group, we believe that these innovations are just the tip of the iceberg. The clinical learnings from AD on optimal diagnosis, delivery, and patient selection will catalyze further innovation in neurodegenerative proteinopathies like Parkinson’s disease, Lewy body dementia, Huntington’s disease, and amyotrophic lateral sclerosis (ALS). This whitepaper uses AD as a case study to explore the future of care in neurodegenerative disease. We present the current understanding of AD disease biology and treatment landscape for a generalist audience and give our vision of the next era of care in AD: a world where AD diagnosis is as easy as blood test and physicians can choose from a broad armamentarium of therapies to tackle the multifaceted features of neurodegeneration.

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Whitepapers

The Limits of Generic LLMs: Why Biotech Needs Purpose-Built Tools

Healthcare is one of the most data-rich and capital-intensive sectors yet remains decades behind in analytics. High-stakes decisions rely on incomplete data and systems that are slow, manual, error-prone, and expensive. The result is billion-dollar missteps, extended timelines in an industry already operating on decade-long horizons, and delays in bringing life-saving medicines to patients. Despite rapid advances in LLMs, these breakthroughs have yet to meaningfully change how strategic decisions are made in biotech. Today’s LLMs are fluent language tools. They can summarize dense papers, extract entities, or polish prose, but language is not the same as evidence-backed strategy. Biotech demands reasoning across fragmented, multi-modal data: trials, patient outcomes, regulatory precedent, and competitive context, all shifting in real time. Addressing this call for platforms that are purpose-built for biotech, fluent in the networked language of biology, and capable of turning vast, messy evidence into actionable, trusted insights. Only with tools like these will strategic decisions in biotech and healthcare match the rigor the field demands. In this whitepaper, we (1) examine how strategic decisions in biotech are made today, (2) show where today’s LLMs help and where they fail, (3) propose a bio-native architecture built on a shared data foundation, multi-hop reasoning, and UX-driven validation loops, and (4) introduce LABI, Luma Group’s AI for Biotech Intelligence, and how we aim to apply these principles in practice.

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Whitepapers

The Case for Venture Capital in Biotechnology

There are many perceptions of what a venture capitalist (VC) is. In the biotech space, VCs are enigmatic because many of us never imagined becoming VCs, and many future VCs might have no idea they’re headed down that path either. My story, like many in biotech, started with a passion to help sick people. I was fortunate to discover my passion at the age of six, when I told my parents that I wanted to be a genetic engineer. I didn’t fully understand what that entailed, but the film Jurassic Park sparked my curiosity. I was fascinated by the idea that nature had invented biological Legos called DNA that could be assembled to create humans, sea slugs, bananas, bacteria, mold, and, most intriguingly, entirely new life forms. Less than two decades later, I received my PhD in Molecular and Cellular Biology. I loved deciphering nature’s clues and genetic codes to figure out the “how” and “why” in nature’s playbook. However, I didn’t yet know how to translate this knowledge from the lab into life-saving therapies. I continued my research journey post-PhD and eventually landed an industry postdoc position at Pfizer. At Pfizer, I soaked up every fact, lesson, piece of jargon, and process required to take an initial discovery and turn it into a drug. At this moment, something clicked for me: my true passion wasn’t just making discoveries; it was figuring out how to transform those discoveries into medicines capable of helping patients. My time at Pfizer also made it clear that I wasn’t ready to join an organization as large as Pfizer long-term. Thankfully, after years of trying different career trajectories and with the help of some great mentors, it became clear that biotech venture capital uniquely aligned both my personal goals: contributing to life-changing therapies, and professional goals: being able to earn a living while pursuing my personal passion. As they say, “Do what you love, and you’ll never work a day in your life.”

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Whitepapers

Data-Driven Biotechnology: How Multi-Omics Analytics Are Shaping the Future of Medicine

Innovation in biotechnology is driven by uncovering novel biological insights and translating them into life-saving therapeutics, diagnostics and medical devices. Over the past two decades, breakthroughs have largely stemmed from analyzing vast biological datasets, such as those generated by human genome projects. Today, advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced our ability to systematically analyze massive datasets, identifying complex relationships across genomic, proteomic, transcriptomic, metabolomic and other data simultaneously. The cross-section of all of these “-omics” is what we define as multi-omics, which represents a large untapped domain for future biotech innovation. The convergence of affordable, sophisticated AI/ML analytics and large-scale multi-omics data collection has marked a pivotal shift within biotechnology, from single-omics approaches to integrated multi-omics innovations.

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