Frameshifts with Benjamin Arya

Alex Zhavoronkov: Beating Aging, Designing Drugs and Betting on China | Frameshifts Episode #12

Plenty of people will tell you AI is about to design our drugs. Alex Zhavoronkov already has. His company’s lead candidate, rentosertib, is the first drug with both its biological target and its molecule discovered by generative AI to post real Phase 2 results in humans. That gap, between the pitch deck and a drug in an actual person, is the whole reason this conversation is worth your time. Alex started out in GPUs and high-performance computing, then left to bet that AI could design drugs better than people can. He retrained in biomedical science, and nearly two decades on, his company Insilico Medicine has gone public in a US$292 million Hong Kong IPO and signed a collaboration with Eli Lilly worth US$2.75 billion. That track record is what makes his bluntness about the rest of the field worth hearing. We get into the only benchmark he thinks matters: the time it takes to go from target identification to a real developmental candidate, and why he treats funding rounds, papers and patents as mostly noise. We talk about how Insilico initially stalled while chasing aging “in the most hardcore way possible”, why he’s convinced that experimental validation rather than model size is still the real bottleneck in biology, and the claim that tends to make founders in Boston and San Francisco uncomfortable: that if you’re not competing and collaborating in China, you’re already behind. In this episode, we also get into: * What “pharmaceutical superintelligence” actually means, and what it doesn’t * Why medicinal chemists aren’t getting replaced any time soon * Why humanoid robots are the wrong bet for the lab right now * Where embodied AI actually fits into biology * Why owning the best software doesn’t make a drug company defensible * How small, validated models end up training the bigger ones * Can you still compete in biotech without building in China? * When AI can design the drug, who actually captures the value? In Alex’s ideal version of all this, you wouldn’t even open a drug discovery platform. You’d just tell a language model what you want cured, for whom, and with what tradeoffs, and let it go to work behind the scenes. It sounds like science fiction, right up until it starts working. GUEST INFORMATION: * Alex Zhavoronkov, CEO Insilico Medicine [https://insilico.com/team] * X (Twitter) [https://x.com/biogerontology?lang=en] * LinkedIn [https://www.linkedin.com/in/zhavoronkov/] * Deep learning enables rapid identification of potent DDR1 kinase inhibitors (Nature Biotechnology, 2019) [https://www.nature.com/articles/s41587-019-0224-x] * Rentosertib Phase IIa results in idiopathic pulmonary fibrosis (Nature Medicine, 2025) [https://www.nature.com/articles/s41591-025-03743-2] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Building Science's Missing Infrastructure — Adam Marblestone | Frameshifts Episode #11

Many of science’s most important projects fall through the cracks between academia and industry. These are the foundational tools that could accelerate entire fields, and yet don’t fit neatly into a PhD thesis or a venture-backed startup. And to humanity’s credit, there has been growing interest in the last few years in building alternative systems for accelerating science beyond academia and industry. Groups like Episteme [https://episteme.com/], the Arc Institute [https://arcinstitute.org/] and Astera [https://astera.org/mission-and-vision] differ in structure and ambition, but they share a common premise: some of the most important work in science requires institutions that neither universities nor venture-backed companies are built to support. They also share something else. None of them would exist without billionaire philanthropy: Altman and Masa in the case of Episteme, Jed McCaleb in the case of Astera, and the Collison brothers in the case of the Arc Institute. And yet when I bring these organizations up with friends and colleagues in science, I often sense the same underlying skepticism: impressive as they are, can any of them really become durable drivers of scientific progress or are they structurally incapable of becoming anything more than donor-dependent experiments? Bell Labs changed the world, but it was built on an economic foundation, a telecommunications monopoly, that no longer exists. So, what’s left in humanity’s armament for progress? Well, there’s the NIH, DARPA and ARPA-H in America, ARIA in the UK, university-affiliated research institutes around the world, a dense ecosystem of startups concentrated in the major entrepreneurial hubs, and then a handful of billionaire-backed nonprofit research orgs. But there is another model that has been gaining traction in recent years: the Focused Research Organization, or FRO. These are nonprofit research organizations built around tightly scoped scientific milestones, typically with 10 to 30 person teams and budgets in the $20-30 million range. Adam Marblestone is the founder of Convergent Research and the architect of the FRO model. Late last year, in what many saw as validation of the model, the National Science Foundation announced a new initiative to “launch and scale a new generation of transformative independent research organizations to advance breakthrough science” [https://www.nsf.gov/news/nsf-announces-new-initiative-launch-scale-new-generation]. In my chat with Adam, he traces his path from graduate training in George Church’s lab to DeepMind’s neuroscience team. He came to believe that science needs a third institutional model, one that complements rather than replaces academia and industry. We discuss the idea of “intellectual dark matter”, the promising ideas researchers have but rarely get the chance to pursue. Adam explains why mathematicians need robust software infrastructure just as much as astronomers need telescopes, and how Convergent Research is systematically identifying more than 100 missing “Hubble Space Telescopes” across scientific fields. Adam argues that many breakthrough ideas remain invisible not because they are wrong, but because the shared infrastructure needed to test them does not yet exist. Topics we cover include: * Why progress in fields like mathematics and neuroscience is often bottlenecked by missing shared infrastructure (e.g. proof verification, connectome mapping, ultrasound brain interfaces) * How “intellectual dark matter” exposes systemic blind spots in the way science is funded, evaluated, and organized * How the Gap Map [https://www.gap-map.org/] is systematically cataloging hundreds of missing foundational capabilities across scientific disciplines * Why building scientific infrastructure often requires industry-style execution inside nonprofit structures * Why some of the most ambitious deep-tech efforts are too infrastructural for venture capital, yet too operational for academia GUEST INFORMATION: * Adam Marblestone, Founder & CEO of Convergent Research [https://www.adammarblestone.org/] * Convergent Research [https://www.convergentresearch.org] * Gap Map [https://www.gap-map.org/] * X (Twitter) [https://twitter.com/AdamMarblestone] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Why 92% of Your Proteins Are Invisible — Parag Mallick | Frameshifts Episode #10

Parag Mallick is a Stanford Professor and Chief Scientist at Nautilus Biotechnology, a publicly traded biotech company. He’s also a professional magician and circus performer, which might sound random until you realize that his company does the closest thing to proteomics magic I’ve ever seen. The things that make you weird, he argues, are exactly what let you see data differently than anyone else.Here’s what caught me off guard: we can only measure ~8% of human proteins with current mass spectrometry tools. The other 92%—what Parag calls the “dark proteome”—is essentially invisible to us.Why is proteomics so much harder than genomics? Three reasons. First, protein concentration. DNA is basically uniform—roughly the same amount per cell. Proteins range from one copy to a billion copies in a single cell, and no analytical tool can handle that spread. Second, dynamics. Your genome is relatively static over your lifetime. Your proteome changes every second of every day. Third, there’s no equivalent of amplifying proteins like PCR can do. You get what you get.Nautilus is tackling this with single-molecule detection. The technical approach is fascinating: they use DNA origami nanoparticles. Picture a Ritz cracker with a flagpole sticking out the top. That flagpole has exactly one attachment point. They coat sample proteins with one half of a click chemistry reagent (methyltetrazine), the nanoparticle has the other half (trans-cyclooctene), and they bind together. One protein per nanoparticle. These then self-assemble onto a flow cell with 100-nanometer landing pads, creating a hyperdense array of billions of individual protein molecules.Now comes the really clever part: protein identification. Traditional proteomics tries to build one highly specific antibody per protein—an intractable problem when you’re dealing with millions of proteoforms. Nautilus does the opposite. They intentionally build cross-reactive affinity reagents. The system uses ~300 different affinity reagents, each one recognizing just a three-amino-acid epitope. That’s deliberately non-specific. Then they run 300 cycles of iterative mapping. The primary data looks like fluorescent NGS—you get a light-up at each location on the array or you don’t. Binary. Yes or no.The median protein only needs ~12 epitopes to be uniquely identified, but each protein gets touched 10-30 times across the 300 cycles for high confidence. It’s exactly like playing Guess Who: “Do you have glasses? Brown hair? A hat?” Each question alone tells you almost nothing, but together they pinpoint exactly who you are. Same principle here: “Do you have this 3-amino-acid sequence? What about this one?”The 300 binary measurements create a point in 300-dimensional space. Each protein has a characteristic signature in this space. The machine learning layer compares your observed pattern against the reference proteome and asks: what protein is compatible with this specific binding pattern? If you find a totally new protein, it’ll occupy a new point in that space—something not in the database.But here’s the thing about building something this audacious: you can’t prove it works before you start. Parag shared what the early days were actually like—renting a single lab bench at Stanford’s StartX incubator, trying to convince people to join when he couldn’t demonstrate single-molecule deposition yet, couldn’t show them they could run 180 cycles because they didn’t have an instrument. The first automation system was literally called “Parag” because he was pipetting by hand. How do you hire people to believe in something impossible? You share the vision of what it could mean—bringing the proteome to everyone—and see if that resonates. Some people thrive in that uncertainty. Others are brilliant at early-stage innovation but different people excel at scaling and productization. That evolution isn’t failure, Parag argues. It’s healthy. It’s part of the journey.We’re really just at the beginning of understanding biology. The genomics revolution, as transformative as it’s been, was the opening act. The era where we can actually see what proteins are doing—that’s what comes next. And in case you’re short on time, here’s a quick teaser: Watch on Youtube [https://www.youtube.com/@Frameshifts/videos]; listen on Apple Podcasts [https://podcasts.apple.com/us/podcast/frameshifts-podcast/id1829721662] or Spotify. [https://open.spotify.com/show/4TTOptQbhlNeT7BMTGpz3S] Guest Information * Parag Mallick [https://med.stanford.edu/profiles/parag-mallick]. Professor at Stanford University & Chief Scientist at Nautilus Biotechnology. * LinkedIn [https://www.biorxiv.org/content/10.1101/2025.06.26.660445v2] * X (Twitter) [https://x.com/paragmallick] * Nautilus Biotechnology [https://www.nautilus.bio/] * Large-scale single-molecule analysis of tau proteoforms [https://www.biorxiv.org/content/10.1101/2025.06.26.660445v2] * Proteomics Toolkit Paper (Nature, 2012) [https://www.nature.com/articles/nbt.2377] * High-density and scalable protein arrays for single-molecule proteomic studies (bioRxiv, 2022) [https://www.biorxiv.org/content/10.1101/2022.05.02.490328v3] Connect With Us * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Building the Wyss Institute, Organs-on-Chips, and Fighting for Science — Don Ingber | Frameshifts Episode #9

The future of human biology isn’t about better animal models - it’s about building human organs on chips that think like the body does. Don Ingber, founding director of Harvard’s Wyss Institute, has one of the most unconventional origin stories in the field. In 1975, as a Yale undergrad, he took a sculpture class where students built floating structures held together only by tension; no rods touching, no rigid frames. At the same time, he watched cells in culture flatten, round up, and then flatten again. The connection clicked: cells aren’t rigid blocks; they’re dynamic tensegrity structures, governed by forces, tension, and mechanical cues. That conceptual shift reshaped his view of biology. Fast-forward, and Ingber’s team would eventually build something that changed the definition of a “model system”: organ-on-chip devices the size of a thumb drive, lined with living human cells, that recapitulate organ-level physiology with astonishing fidelity. Key Takeaways: * Animal testing has a low success rate: 70% of clinical trials fail, with neurology hitting 95% failure rate. Organs-on-chip technology offers an alternative method that can help predict patient treatment responses * How a chip mimics an organ: Two microfluidic channels with different tissue types separated by a porous membrane. Cyclic suction mimics breathing or peristalsis * The microbiome breakthrough: Culturing complex bacterial communities for days using flow and mechanical forces creates a microbiome-like culture compared to static cultures * Personalized medicine economics: Test 100 patient-derived chips, identify the 50 responders, screen for toxicity, run focused clinical trials on the 35 most promising candidates * The Wyss origin story: A donor who walked away for two years, and a Martha’s Vineyard meeting that led to a $125 million gift * Why institutional structure matters: Independent governance, no deans, separate finances from Harvard and MIT * The Boston/Cambridge singularity: Universities, hospitals, VCs, pharma all in walking distance. You can’t recreate this by distributing talent globally * What’s at stake now: Immigration restrictions and funding cuts threaten the human capital that drives American innovation This conversation goes beyond the science. We get into what it actually takes to build an institution like the Wyss, and how we’re watching the American scientific leadership face an existential threat with immigration restrictions and ideological constraints. And in case you’re short on time, here’s a quick teaser: Watch on Youtube [https://www.youtube.com/@Frameshifts/videos]; listen on Apple Podcasts [https://podcasts.apple.com/us/podcast/frameshifts-podcast/id1829721662] or Spotify. [https://open.spotify.com/show/4TTOptQbhlNeT7BMTGpz3S] GUEST INFORMATION: * Don Ingber - Founding Director, Wyss Institute for Biologically Inspired Engineering at Harvard University [https://wyss.harvard.edu/team/core-faculty/donald-ingber/] * “Cellular tensegrity: defining new rules of biological design that govern the cytoskeleton” - Journal of Cell Science (1993) [https://pubmed.ncbi.nlm.nih.gov/8314865/] * “Human organs-on-chips for disease modelling, drug development and personalized medicine” - Nature Review Genetics (2022) [https://www.nature.com/articles/s41576-022-00466-9] * X (Twitter) [https://x.com/DonIngber] * Wyss Institute [https://wyss.harvard.edu] * Emulate (Organ Chip Company) [https://emulatebio.com] CONNECT WITH US: * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]

Shelby Newsad: Why Platforms Fail, What Founders Need, and Where Biotech Wins | Frameshifts Episode #8

This week, I sat down with Shelby Newsad, Partner at Compound VC, to break down what makes a great biotech investment and why so many platform stories fall short. Shelby lays out a playbook forged across therapeutics, diagnostics, and lab automation. We dive into why mechanistic understanding predicts success, how diagnostics evolve into data businesses, and why the next wave of value may come from closed loop automated labs that combine hardware and software to finally link the whole bench. We explore faster paths to human evidence through compassionate use, phase 0 trials, and cassette microdosing, and how biobanks move discovery closer to patients. Shelby also explains how RNA secondary structure targeting with covalent small molecules could merge phenotypic and target based discovery inside one company. Then things get wonderfully weird. We discuss manufacturing as value capture in a world where intelligence designs drugs, gene edited flowers as a low regulation high margin venture canvas, and what it means when biohackers and LLMs push healthcare outside traditional borders. Key Takeaways * Mechanism > platform: Companies prosecuting mechanistic disease biology have a 3–4× higher chance of success than platform first bets with fuzzy “why.” * Diagnostics as engines: Tests that are profitable and build biobanks create data and network effects and decouple cost from revenue via services and licensing. * Human evidence faster: Use compassionate use, phase 0, and cassette microdosing to de risk earlier with real PK PD and binding data. * Automation gap: Most lab robots lack vision systems. Closed loop labs that are miniaturized and observable are the usability unlock. * RNA covalency: Covalent binders to RNA secondary structures offer clean transcriptome readouts blending phenotypic and target based discovery. * Value capture shift: If AI designs the drug, manufacturing of proteins, chemicals, and gene therapies could own the margin stack. * Global arbitrage: The United States leads, but Australia and China accelerate timelines. Trials and approvals follow speed and clarity. * Weird is good: Gene edited flowers that are non edible and simpler to regulate marry beauty, margins, and scale, unexpected but venture real. But in case you’re short on time, here’s a quick teaser: Watch on YouTube [https://youtu.be/i9YKjd0RWw0]; listen on Apple Podcasts [https://podcasts.apple.com/us/podcast/frameshifts-podcast/id1829721662] or Spotify [https://open.spotify.com/show/4TTOptQbhlNeT7BMTGpz3S?si=d629483e727e4b2d]. 🔔 SUBSCRIBE so you don’t miss our next conversation! GUEST INFORMATION: * Website [https://www.compound.vc/team/shelbyn] * LinkedIn [https://www.linkedin.com/in/shelbynewsad/] * X (Twitter) [https://x.com/shelbynewsad] CONNECT WITH US : * Website [https://frameshifts.org/] * Substack [https://frameshifts.bio/] * YouTube [https://www.youtube.com/@Frameshifts] * X (Twitter) [https://x.com/frameshiftspod] * LinkedIn [https://www.linkedin.com/company/frameshifts/] * TikTok [https://www.tiktok.com/@frameshiftspod] Get full access to Frameshifts with Benjamin Arya at frameshifts.bio/subscribe [https://frameshifts.bio/subscribe?utm_medium=podcast&utm_campaign=CTA_4]