Sponsor note: I am extremely happy to announce my first commercial, service-oriented sponsor: rush.cloud. I’ve been doing these podcasts entirely through very kind philanthropic graces, which is very nice, but I’d ideally like to be helping someone when they sponsor me. And now I have that! So, if you are at all involved doing preclinical drug discovery and would benefit from computational tools, you should check out their platform + beautiful website here: rush.cloud.

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Youtube: https://www.youtube.com/watch?v=W0m3Ltz_YqU

Apple Podcasts: https://podcasts.apple.com/us/podcast/owl-posting/id1758545538?i=1000736122646

Spotify: https://open.spotify.com/episode/5l9RMbMwdgOrrZ6uLS656R?si=938af7d2b79440a1

Transcript: https://www.owlposting.com/p/can-machine-learning-enable-100-plex?open=false#%C2%A7transcript

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Introduction:

Ellen Zhong is perhaps one of the only people in the ML x bio field to have created an entirely new subfield of research during her PhD: the application of deep-learning to cryo-EM particle images.

If you aren’t familiar with that field, I luckily have a 8,000~ word article covering it, which walks through a lot of Ellen’s papers. If you don’t have time to read something that grossly large, the general breakdown of the problem is as follows: cryo-EM can give you thousands of 2D views of a 3D protein from many different angles, from that data, can you discover what that 3D structure is? Ellen, who is a computer science professor at Princeton University, has spent her academic career investigating that question, and now has an entire lab at Princeton (E.Z. Lab) focused on that and related ones. Including, as the title mentions, the possibility of doing performing cryo-EM structure determination at ultra-high scales.

In this podcast, we talk about her research, what she did during her recent sabbatical at Generate:Biomedicines, her recent interest in areas beyond cryo-EM (cryo-ET and NMR specifically), and more!

Timestamps

[00:00:00] Introduction

[00:02:43]  What does it mean to apply ML to cryo-EM?

[00:04:28] Ab initio reconstruction and conformational heterogeneity

[00:15:41] Can we do multiplex cryo-EM structure determination?

[00:22:19] Datasets in cryo-EM

[00:26:25] Why isn’t there a foundation model for cryo-EM particle analysis?

[00:33:07]  How much practical usage is there of these cryo-EM models amongst wet-lab cryo-EM researchers?

[00:40:34] Where can things still improve?

[00:46:57]  Has deep learning done something in cryo-EM that was previously impossible?

[00:48:22] Ellen’s experience in the cryo-EM field

[00:53:40] Deep learning in cryo-EM outside of structure determination

[00:57:32] 3D volume reconstruction versus residue assignment in cryo-EM

[01:00:26] What did Ellen do during her sabbatical at Generate Biomedicines?

[01:07:07] Ellen’s research in cryo-ET

[01:13:54] Ellen’s research in NMR

[01:21:05] How did Ellen get into the cryo-EM field?

[01:26:57] Why did Ellen go back to graduate school?

[01:32:17]  What makes Ellen more confident about trusting an external cryo-EM paper?

Note: Extremely grateful for Geltor (http://geltor.com/) for sponsoring this podcast, and for the founder of it (https://www.linkedin.com/in/alexanderlorestani) for reaching out to make to start with! Geltor produces designer proteins for beauty and wellness.

The current in-vogue thing to do for most longevity companies is to go for cellular reprogramming. As in, fill a cell with the right transcription factors needed to reduce epigenetic noise, restore mitochondrial dysfunction, and so on. I’ve written about the promise there before, it’s definitely an exciting field.

So, when I first met Alan— who told me that he was a longevity researcher — last October, I naively assumed he was also on the reprogramming train. But he told me that he was investigating something a bit different. His pitch was that, instead of reprogramming the cell to fix age-related damage, what if you just protected it from (genetic) insult first? It’s an obvious idea, but one that I’d never really deeply considered. He sold me on the concept, and I was very curious to hear what he’d do next to push it forwards.

A few months after our chat, he spun up a company to pursue this line of thinking: Permanence Bio, which develops molecules that stabilize/protect the genome. They are just about eight months old, but there are already some exciting results coming out. I’m a sucker for people doing ‘contrarian research in consensus fields’, and I immediately knew I wanted to have Alan on the podcast. He graciously agreed and, during my trip to SF last month, we sat down and talked for a few hours.

In this episode, we talk about why DNA protection is so important, what indications is it useful for, how to mentally conceptualize the idea of a molecule ‘stabilizing’ a genome, what it was like to raise money for a company pursuing such an out-of-distribution thesis, and lots more.

Finally, Alan has a really great blog (something I mention in the video), and I wanted to attach a much longer article he’s written about the topic here.

[00:00:00] Teaser clip

[00:01:39] Introduction

[00:07:32] What is Permanence working on?

[00:11:48] What does DNA protection actually look like?

[00:27:12] Why is DNA protection not focused on as much?

[00:41:03] The utility of epigenetic clocks

[00:46:47] Do you need multimechanism approaches for longevity?

[00:51:58] Longevity outside of DNA protection

[00:55:57] What's going on inside of Permanence?

[01:05:54] How could Permanence fail?

[01:09:03] How do you stay optimistic?

[01:10:26] Why work on aging?

[01:15:26] What are you bearish on?

[01:19:12] Weirder types of aging beyond 110

[01:21:37] How did you decide on DNA protection and what else would you have done?

[01:25:27] What was it like raising money?

[01:31:48] What do you think of past cancer prevention trials?

[01:34:12] What does good wet-lab talent look like?

[01:37:02] What does your information diet look like?

[01:40:06] What's it like going from research to being a CEO?

[01:42:20] What happens after cancer prevention for Permanence?

X: https://x.com/owl_posting

Sergey's X: https://x.com/sokrypton

Youtube: https://youtu.be/6_RFXNxy62c

Spotify: https://open.spotify.com/episode/0wPs3rmp0zrfauqToozrcv?si=DCtRf-xQTPiVYwslo-b2rQ

Apple Podcasts: https://podcasts.apple.com/us/podcast/what-could-alphafold-4-look-like-sergey-ovchinnikov-3/id1758545538?i=1000704927828

Transcript: https://www.owlposting.com/p/what-could-alphafold-4-look-like?open=false#%C2%A7transcript

To 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

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].

Timestamps

01:31 Introduction

02:38 Why is there such little biotech research in India?

17:43 Advantages of building a company in India

31:30 Policy prescriptions for India

35:39 Questions on vaccine design

50:55 What does PopVax do?

01:01:58 The role of machine learning in vaccine design

01:12:07 The (conservative) culture of vaccinology

01:26:57 Hiring in India

01:46:52 How fundraising for an Indian vaccine design startup is coming along

01:57:36 How is PopVax so good at designing vaccines?

02:02:07 Pet theories on immune mechanisms

02:09:07 mRNA beyond infectious diseases

02:12:38 What would you do with $100 million dollars?

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-explanation

Transcript of this episode (contains links to all referenced organizations and papers)

My Twitter

My Substack (you should subscribe!)

Timestamps:

00:00 Introduction

01:19 Divide between classical and quantum simulation

03:48 What are NNP's actually learning?

06:02 What will NNP's fail on?

08:08 Short range and long range interactions in NNP's

10:23 Emergent behavior in NNP's

16:58 Enhanced sampling

18:16 Cultural distinctions in NNP's for life-sciences and material sciences

21:13 Gap between simulation and real-life

36:18 Benchmarking in NNP's

41:49 Is molecular dynamics actually useful?

53:14 Solvent effects

55:17 Quantum effects in large biomolecules

57:03 The legacy of DESRES and Anton

01:02:27 Unique value add of simulation data

01:06:34 NNP's in material science

01:13:57 The road to building NNP's

01:21:13 Building the SolidWorks of molecular simulation

01:30:05 Simulation workflows

01:41:06 The role of computational chemistry

01:44:06 The future of NNP's

01:51:23 Selling to scientists

02:01:41 What would you spend 200 million on?