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Owl Posting

Abhishaike Mahajan
Owl Posting
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  • What could Alphafold 4 look like? (Sergey Ovchinnikov, Ep #3)
    X: https://x.com/owl_postingSergey's X: https://x.com/sokryptonYoutube: https://youtu.be/6_RFXNxy62cSpotify: https://open.spotify.com/episode/0wPs3rmp0zrfauqToozrcv?si=DCtRf-xQTPiVYwslo-b2rQApple Podcasts: https://podcasts.apple.com/us/podcast/what-could-alphafold-4-look-like-sergey-ovchinnikov-3/id1758545538?i=1000704927828Transcript: https://www.owlposting.com/p/what-could-alphafold-4-look-like?open=false#%C2%A7transcriptTo those in the protein design space, Dr. Sergey Ovchinnikov is a very, very well-recognized name.A recent MIT professor (circa early 2024), he has played a part in a staggering number of recent innovations in the field: ColabFold, RFDiffusion, Bindcraft, automated design of soluble proxies of membrane proteins, elucidating what protein language models are learning, conformational sampling via Alphafold2, and many more. Of course, all these papers were group efforts, but Sergey's name comes up astonishingly frequently! And even beyond the research that have come from his lab in the last few years, the co-evolution work he did during his PhD/fellowship also laid some of the groundwork for the original Alphafold paper, being cited twice in it.As a result, Sergey’s work has gained a reputation for being something that is worth reading. But nobody has ever interviewed him before! Which was shocking for someone who was so pivotally important for the field.So, obviously, I wanted to be the first one to do it. After an initial call, I took a train down to Boston, booked a studio, and chatted with him for a few hours, asking every question I could think of. We talk about his own journey into biology research, some issues he has with Alphafold3, what Alphafold4-and-beyond models may look like, what research he’d want to spend a hundred million dollars on, and lots more. Take a look at the timestamps to get an overview!Final note: I’m extremely grateful to Asimov Press for helping fund the travel + studio time required for this episode! They are a non-profit publisher dedicated to thoughtful writing on biology and metascience, such as articles over synthetic blood and interviews with plant geneticists. I myself have published within them twice! I highly recommend checking out their essays at asimov.press, or reaching out to [email protected] if you’re interested in contributing.Timestamps:[00:00:00] Highlight clips[00:01:10] Introduction + Sergey's background and how he got into the field[00:18:14] Is conservation all you need?[00:23:26] Ambiguous vs non-ambiguous regions in proteins[00:24:59] What will AlphaFold 4/5/6 look like?[00:36:19] Diffusion vs. inversion for protein design[00:44:52] A problem with Alphafold3[00:53:41] MSA vs. single sequence models[01:06:52] How Sergey picks research problems[01:21:06] What are DNA models like Evo learning?[01:29:11] The problem with train/test splits in biology[01:49:07] What Sergey would do with $100 million Get full access to Owl Posting at www.owlposting.com/subscribe
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  • How do you make a 250x better vaccine at 1/10 the cost? Develop it in India. (Soham Sankaran, Ep #2)
    This is an interview with Soham Sankaran, the founder and CEO of PopVax, an mRNA vaccine development startup.Curiously, PopVax is based in India, specifically Hyderabad. This should be a surprise to most people in the field: we never really hear of interesting biotech research being done in a place that isn’t [US, Europe, East Asia].Yet, PopVax has been astonishingly successful, having a (in mouse) influenza vaccine that is 250x better than its competitors, multiple large research collaborations, and their first upcoming US based phase 1 clinical trial being fully sponsored and conducted by the NIH.It’s an extremely interesting success story from what feels like a very clear underdog. In this 2-hour podcast, we discuss everything from why so little biotech research gets done in India, a breakdown on what you care about in vaccine design (immunogens), how PopVax uses machine learning for precise immunogen design, how raising money for a vaccinology startup is going, and a lot more.Timestamps and transcripts are below. Just as in my last episode, I’ve included a ‘jargon explanation’ as a quick primer for some of the subjects discussed in the episode.Some final bits: the studio rental costs were kindly covered by Dylan Reid! Huge shout-out to him for making this episode possible. Also shout-out to Samarth Jajoo, Reha Mathur, and David Yang for some very helpful discussion about the Indian biotech scene. And, if you think PopVax is interesting, here is their Substack which has some articles on their results, their job section (they are actively hiring), and can be reached at [email protected]:31 Introduction02:38 Why is there such little biotech research in India?17:43 Advantages of building a company in India31:30 Policy prescriptions for India35:39 Questions on vaccine design50:55 What does PopVax do?01:01:58 The role of machine learning in vaccine design01:12:07 The (conservative) culture of vaccinology01:26:57 Hiring in India01:46:52 How fundraising for an Indian vaccine design startup is coming along01:57:36 How is PopVax so good at designing vaccines?02:02:07 Pet theories on immune mechanisms02:09:07 mRNA beyond infectious diseases02:12:38 What would you do with $100 million dollars? Get full access to Owl Posting at www.owlposting.com/subscribe
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  • Can AI improve the current state of molecular simulation? (Corin & Ari Wagen, Ep #1)
    In my first (real) podcast episode, I talk with Corin and Ari Wagen, two brothers who I met through my writing. They are building something super cool: a molecular simulation company called Rowan (which recently got into the Nat Friedman AI grant program). We discuss neural network potentials (NNP’s), whether dynamics are useful at all, the role of computational chemistry in drug design, what the future of the field looks like for molecular simulation, and a lot more.If you work in molecular simulation, I recommend trying out their tool at rowansci.com. I’m not a chemist and cannot vouch for the tool personally, but I can vouch for how much I’d trust Corin and Ari to build something useful. Not a paid sponsorship, not anything I have an investment in, etc, etc, I just genuinely want their startup to succeed.If you're confused by this episode, check out the 'Jargon Explanation' on the Substack post: https://www.owlposting.com/i/152329408/jargon-explanationTranscript of this episode (contains links to all referenced organizations and papers)My TwitterMy Substack (you should subscribe!)Timestamps:00:00 Introduction01:19 Divide between classical and quantum simulation03:48 What are NNP's actually learning?06:02 What will NNP's fail on?08:08 Short range and long range interactions in NNP's10:23 Emergent behavior in NNP's16:58 Enhanced sampling18:16 Cultural distinctions in NNP's for life-sciences and material sciences21:13 Gap between simulation and real-life36:18 Benchmarking in NNP's41:49 Is molecular dynamics actually useful?53:14 Solvent effects55:17 Quantum effects in large biomolecules57:03 The legacy of DESRES and Anton01:02:27 Unique value add of simulation data01:06:34 NNP's in material science01:13:57 The road to building NNP's01:21:13 Building the SolidWorks of molecular simulation01:30:05 Simulation workflows01:41:06 The role of computational chemistry01:44:06 The future of NNP's01:51:23 Selling to scientists02:01:41 What would you spend 200 million on? Get full access to Owl Posting at www.owlposting.com/subscribe
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a podcast about biology and computation. transcripts on https://www.owlposting.com/s/podcast! www.owlposting.com
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