Curing All Disease with AI with Max Jaderberg

Can AI help us model biology down to the molecular level? Neil deGrasse Tyson, Chuck Nice, and Gary O’Reilly learn about Nobel-prize-winning Alphafold, the protein folding problem, and how solving it could end disease with AI researcher, Max Jaderberg.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Neil deGrasse Tyson?

  So AI was not satisfied just whooping our ass in chess and in Jeopardy and everything else where it looks like brains mattered. It's now taken over our physiology.

• 0:10Gary O'Reilly?

  Well, no, you pointed it in a good direction, aimed it at a good place and we're getting someone.

• 0:14Neil deGrasse Tyson?

  To solve our diseases.

• 0:15Chuck Nice?

  Yeah, so now it's going to cure us of all disease before it makes us its slaves.

• 0:22Gary O'Reilly?

  Cause we need healthy slaves.

• 0:24Neil deGrasse Tyson?

  All that and more coming up on StarTalk.

• 0:29Neil deGrasse Tyson?

  Welcome to StarTalk, your place in the universe where science and pop culture collide. StarTalk begins right now.

• 0:42Neil deGrasse Tyson?

  This is StarTalk, special edition. Neil deGrasse Tyson, your personal astrophysicist. Special edition means we've got Gary O'Reilly in the house. Gary.

• 0:52Gary O'Reilly?

  Hi Neil.

• 0:52Neil deGrasse Tyson?

  Former soccer pro.

• 0:53Gary O'Reilly?

  Apparently.

• 0:54Neil deGrasse Tyson?

  Yeah, and soccer announcer.

• 0:55Gary O'Reilly?

  Yes, definitely.

• 0:57Neil deGrasse Tyson?

  And you still do that, don't you?

• 0:58Gary O'Reilly?

  I do.

• 0:59Neil deGrasse Tyson?

  Chuck Nice, baby.

• 1:00Chuck Nice?

  Hey, announcing that I know nothing about soccer.

• 1:04Gary O'Reilly?

  You're in my club then.

• 1:05Neil deGrasse Tyson?

  Announcing that you are American.

• 1:07Chuck Nice?

  American dove on a grill football, violence.

• 1:12Neil deGrasse Tyson?

  So we're talking about AI today. It's a favorite topic. We revisit that often.

• 1:17Chuck Nice?

  Only, only the future of the entire world.

• 1:20Neil deGrasse Tyson?

  AI as it matters in biology.

• 1:23Chuck Nice?

  Oh, wow. Now that's a big deal.

• 1:25Neil deGrasse Tyson?

  I know, I know. Cause people thinking about, you know, composing your term paper or winning a chess. But it's got a whole frontier ready to be explored.

• 1:36Chuck Nice?

  Yeah.

• 1:37Neil deGrasse Tyson?

  And so tell me what you and your producers cooked up today.

• 1:40Gary O'Reilly?

  Okay, so we've been on the case to get these guys involved for some time, but they are so busy. So here we go. I'll say it. I'm made of proteins. Yes. You're made of proteins from strings of amino acids that fold into shapes that put all together form us. But there's a fundamental problem in biology. That has implications for all of medicine. How do these proteins fold up?

• 2:06Chuck Nice?

  Oh.

• 2:07Gary O'Reilly?

  For this solution, we looked to AI and a Google DeepMind tool called AlphaFold. The second iteration of AlphaFold 2 won the Nobel Prize in Chemistry last year for answering this very question. Who knew AI was smart, huh?

• 2:22Neil deGrasse Tyson?

  I appreciate AI win all the Nobel prizes.

• 2:24Gary O'Reilly?

  Yeah, just get them now. Just pack them all up.

• 2:26Neil deGrasse Tyson?

  Pack them up.

• 2:27Gary O'Reilly?

  Now, the Isomorphic Labs, together with Google DeepMind, developed and released AlphaFold 3. Yes, we're on the third iteration. And that was last year and applied these new AI models for drug discovery.

• 2:38Chuck Nice?

  Oh, that's great.

• 2:39Gary O'Reilly?

  All right, so think this through. Could our next generations of treatments be computer generated?

• 2:45Chuck Nice?

  Oh, yeah.

• 2:46Gary O'Reilly?

  Oh, by the way, Neil, let's introduce our guest.

• 2:48Neil deGrasse Tyson?

  I will. And we've got Max Yadeberg. Did I pronounce that correctly?

• 2:53Speaker 03

  Yeah, you got it. You got it right.

• 2:54Gary O'Reilly?

  Let's hear you say it. Let's hear you say it.

• 2:56Speaker 03

  Me say it.

• 2:57Gary O'Reilly?

  Yeah.

• 2:57Speaker 03

  Max Yadeberg.

• 2:58Gary O'Reilly?

  Oh, he got it perfect.

• 2:59Neil deGrasse Tyson?

  That's what I got.

• 3:00Gary O'Reilly?

  He copied you.

• 3:01Speaker 03

  He was practicing. He was practicing.

• 3:03Neil deGrasse Tyson?

  So you studied AI at Oxford?

• 3:05Speaker 03

  That's right. That's right.

• 3:06Chuck Nice?

  No, I hear that's a, that's a community college?

• 3:09Neil deGrasse Tyson?

  Oxford, Oxford Community College. That's exactly right. In Oxford, England. Yes.

• 3:15Neil deGrasse Tyson?

  So, specializes in deep learning algorithms. I got your little bio here. For understanding images.

• 3:22Chuck Nice?

  Nice.

• 3:22Neil deGrasse Tyson?

  That was a big advance when, when a search can go into an image. I thought, you know, I died and gone to computer heaven where that started.

• 3:30Speaker 03

  Yeah, yeah. I mean, you know, this, this was 10, 15 years ago, back before AI was cool.

• 3:35Neil deGrasse Tyson?

  Right, right.

• 3:36Speaker 03

  Where, you know, you talk about AI and it's something from a sci-fi book, but understanding images and videos was like the big thing at that point in time. We couldn't, we couldn't actually do that very well.

• 3:45Neil deGrasse Tyson?

  I searched my 9,000 images on my computer for the word telescope.

• 3:49Speaker 03

  Yeah.

• 3:50Neil deGrasse Tyson?

  And it found telescope written in Chinese.

• 3:53Speaker 03

  That's crazy.

• 3:54Neil deGrasse Tyson?

  On a photo taken at an angle in one of my images.

• 3:58Chuck Nice?

  Wow.

• 3:59Neil deGrasse Tyson?

  When I was visiting China.

• 4:00Speaker 03

  Yeah, this is it. Like during my PhD, we took all of the BBC's back catalog.

• 4:05Gary O'Reilly?

  No.

• 4:06Speaker 03

  And we ran my algorithm across it and created a search engine. So you could pull up. Footage from decades ago that had this text or these objects. That's seriously some archive.

• 4:17Chuck Nice?

  So here's what I'm interested, when you do that, do you tie? So when AI is looking at an image, it's not seeing the image the way we do. We're not even seeing a whole image. Our brains, we're really just intuiting an image when we see it.

• 4:31Speaker 03

  Exactly, exactly.

• 4:32Chuck Nice?

  That's how we do it. But the AI actually-

• 4:34Speaker 03

  It's like a holistic processing.

• 4:35Chuck Nice?

  It's a holistic processing. AI actually sees the image and what it's seeing is pixels.

• 4:40Speaker 03

  That's right.

• 4:40Chuck Nice?

  And that really all it's doing is just, oh, this pixel, this pixel, this pickle, in this arrangement, that's this image. So do you tie that to language and that's how we search? Or is the search just the AI knows the actual image itself?

• 4:55Speaker 03

  This was like the big breakthrough back then when I was doing my PhD. And this is what deep learning as well is all about.

• 5:01Chuck Nice?

  Right.

• 5:01Speaker 03

  You can imagine if you have this pics, this image full of pixels, how do you actually code up how to read text from there?

• 5:07Chuck Nice?

  Right.

• 5:08Speaker 03

  How do you tie it to the language of the text?

• 5:09Chuck Nice?

  Right.

• 5:10Speaker 03

  It's unimaginably hard to code that up by hand.

• 5:13Chuck Nice?

  Yes.

• 5:13Speaker 03

  So instead, what you do is you put these, what they're called neural networks. They look at all of the pixels of the image and you give it lots and lots of examples, images that have somewhere it's got the text in it. And you tell the neural network what the text is.

• 5:28Chuck Nice?

  I see.

• 5:29Speaker 03

  And the neural network, through lots and lots of training, starts to work out its internal algorithm to extract the information from these pixels, piece it all together and spit out the actual text or spit out what the objects are.

• 5:41Chuck Nice?

  Wow.

• 5:42Neil deGrasse Tyson?

  So you're currently chief AI officer at Isomorphic Labs. This is a biology place.

• 5:48Speaker 03

  That's right.

• 5:48Neil deGrasse Tyson?

  Do you have any biology in your background? Formal biology?

• 5:51Speaker 03

  No.

• 5:52Neil deGrasse Tyson?

  No, okay. So they want you for your AI.

• 5:54Speaker 03

  That's right, that's right. So I was at a place called DeepMind beforehand.

• 5:57Neil deGrasse Tyson?

  Oh, Google.

• 5:58Speaker 03

  Google DeepMind, yes, exactly. I was there for a long time. I absolutely love this core AI technology called deep learning. That's what I've been developing my whole career so far. At DeepMind, we were working on some crazy stuff, you know, learning to play chess and Go and beating top professionals at games like StarCraft. Back then, it was about-

• 6:17Chuck Nice?

  It was a big deal.

• 6:17Speaker 03

  Yeah, and because the world didn't know what AI was, so we were trying to prove that this was even a thing, right? It seems crazy now, but back then it was just proving that this was actually a real thing. But at the core, you know, I love this technology. I want to see it have profound impact on our world. And I was thinking these things.

• 6:34Neil deGrasse Tyson?

  That's where it begins.

• 6:35Chuck Nice?

  Yep.

• 6:36Neil deGrasse Tyson?

  That's where the Terminator starts.

• 6:38Chuck Nice?

  It's always the innocent dreamer. Who says, this can change the world for such good.

• 6:44Neil deGrasse Tyson?

  And it's in my closet now. Would you like to see?

• 6:46Chuck Nice?

  Exactly. And then it's always like some evil businessman who's just like, with my weather machine, I will one day rule the world, you know, so.

• 6:54Gary O'Reilly?

  Apart from that, carry on.

• 6:55Speaker 03

  Yes. The good thing is, there's some pretty strictly good applications of AI that we can drive. And, um, you know, Demis Hassabis.

• 7:03Gary O'Reilly?

  Yes.

• 7:03Speaker 03

  Started Isomorphic Labs, spinning that out of Google DeepMind, to really think, how can we apply AI to actually, you know, completely solve all disease?

• 7:13Neil deGrasse Tyson?

  Okay, so it has genetic links back to its origin story.

• 7:16Speaker 03

  Exactly, exactly.

• 7:17Neil deGrasse Tyson?

  It was in DeepMind. I feel better about that now.

• 7:19Gary O'Reilly?

  Yeah.

• 7:19Neil deGrasse Tyson?

  Okay.

• 7:20Gary O'Reilly?

  You happy?

• 7:20Neil deGrasse Tyson?

  Yeah, I'm happier now. Yes.

• 7:22Speaker 03

  So I moved over as part of that founding team to head up AI in this space. And, um, you know, it's been about three and a half years now. Been a crazy journey, but it's fascinating. It's so much fun.

• 7:30Neil deGrasse Tyson?

  So you got this AI expertise and Alpha Fold spins off this biological application of it. First, tell me the word isomorphic. What does that mean in biology?

• 7:41Speaker 03

  Isomorphic is this, is this technical term, which is, you know, a one-to-one mapping of, of, of space, right? And the reason we're called Isomorphic Labs is, is really that we believe that biology is really, really complicated. In, in, in the world of physics, we can write down equations for physics with maths. And maths is that perfect description language for physics. But you can't really just write down equations in maths for biology, for the cell. It's just too complicated.

• 8:06Neil deGrasse Tyson?

  Biology is the most complex expression of chemistry.

• 8:09Chuck Nice?

  Yes.

• 8:10Neil deGrasse Tyson?

  That we know.

• 8:10Speaker 03

  There's just, there's just so many moving parts.

• 8:12Chuck Nice?

  Oh, did you just make that up?

• 8:13Neil deGrasse Tyson?

  Yeah.

• 8:14Gary O'Reilly?

  That's really good. So we're looking for a Rosetta Stone here for, for the language of biology.

• 8:18Speaker 03

  Exactly. So, so what could be that perfect description language for biology? We believe AI and machine learning is that.

• 8:23Chuck Nice?

  Right.

• 8:24Speaker 03

  So that there could exist an isomorphism, a mapping between the biological world and the world of AI machine learning.

• 8:31Neil deGrasse Tyson?

  Hence the name. All right.

• 8:32Chuck Nice?

  Gotcha, gotcha.

• 8:33Neil deGrasse Tyson?

  All right. So, uh, tell us about protein folding. Because, you know, when we learn about chemistry, we learn about chemical reactions. And we're not really taught that the shape of the molecule should have anything to do with anything. It's just what is the chemical symbol?

• 8:49Speaker 03

  And when you write down the chemical equations, there's no shape in there. There's just what elements and molecules comprise it.

• 8:58Chuck Nice?

  And those equations don't really ever represent the three dimensional nature.

• 9:05Speaker 03

  Exactly. You don't even know if it has handedness, right? So take us from there. We think about proteins. Proteins are these fundamental building blocks of life.

• 9:14Chuck Nice?

  Yeah.

• 9:15Speaker 03

  They're inside of everyone. They make up everything we have, basically.

• 9:18Chuck Nice?

  Mm hmm.

• 9:19Speaker 03

  And they're made up of what's called a sequence of amino acids. Each amino acid is a molecule. There's about 20 different amino acids and you put them together in a long. Ever or just in life? In life, you can have non-natural amino acids as well that you can make as well.

• 9:34Chuck Nice?

  You can make them.

• 9:35Speaker 03

  You can make them and actually use those for drugs sometimes. You string these amino acids together and that becomes a protein. But they don't exist as these strings. They fold up spontaneously in the cell.

• 9:46Chuck Nice?

  Right.

• 9:46Speaker 03

  To create these 3D shapes. And why that's important is that these proteins, they're basically molecular machines. They don't just exist by themselves. They actually like create these little pieces of machinery. They interact with other proteins. They interact with other biomolecules like DNA and RNA. Right. That interaction is a shape fitting. Exactly. Exactly. So these proteins. It's a puzzle. It's a 3D puzzle. It's a 3D puzzle.

• 10:10Chuck Nice?

  Exactly.

• 10:11Speaker 03

  A 3D jigsaw puzzle. And it's not. Which is way harder than a 2D jigsaw puzzle. And these are not static things. It's not just static puzzle pieces coming together. They change shapes. So something comes in contact and that opens up something else on the other side of the protein, which changes the machine and on and on it goes.

• 10:25Gary O'Reilly?

  And that's what I was gonna ask you. What speeds is this folding taking place? Is it continuous? Once it folds, that's it? But you've just told me, no, it just keeps moving through the whole thing.

• 10:36Speaker 03

  Yeah. These are really, really complex, dynamical systems.

• 10:39Chuck Nice?

  Yeah.

• 10:40Speaker 03

  You know, composed of thousands, millions, trillions of atoms within our cells. Unfolding over the course of, you know, microseconds and beyond.

• 10:47Chuck Nice?

  And this dinacism that you're talking about.

• 10:49Neil deGrasse Tyson?

  What word is that?

• 10:51Chuck Nice?

  Dinacism?

• 10:52Neil deGrasse Tyson?

  That's a word?

• 10:52Chuck Nice?

  Isn't that a word?

• 10:53Neil deGrasse Tyson?

  Dinacism?

• 10:54Chuck Nice?

  I think I just made it up. Mism. Oh, dinanism.

• 10:57Neil deGrasse Tyson?

  No, dynamism.

• 10:58Chuck Nice?

  Dynamism.

• 10:59Neil deGrasse Tyson?

  Thank you, okay.

• 11:00Chuck Nice?

  I'm correcting grammar. This is a first. This is.

• 11:04Chuck Nice?

  Dynamism.

• 11:04Neil deGrasse Tyson?

  Dynamism. What kind of din- dinosaur? Dynamism?

• 11:08Chuck Nice?

  Not dinosaur. Not dinosaur. Not dinosaur. But the dynamism that you're talking about within the cell, when you look at each one of us, since each one of us is so different, even though there's a general, like, execution and blueprint, we all come out so different. Is that part of the process that you are looking at and mapping?

• 11:31Neil deGrasse Tyson?

  I, I would say, to a jellyfish, we all look identical.

• 11:35Chuck Nice?

  True.

• 11:35Neil deGrasse Tyson?

  Okay? They're not saying, oh, is your skin color slightly different? Or you're slightly taller?

• 11:40Chuck Nice?

  Yes.

• 11:40Neil deGrasse Tyson?

  You're describing functions at a cellular level. Is your job to understand that? Or is your job to figure out extra ways to fold proteins that maybe biology has yet to even figure out?

• 11:53Chuck Nice?

  Ooh.

• 11:53Neil deGrasse Tyson?

  That can then solve problems that we encounter that the natural universe has not. So, ooh, that was a good question.

• 12:03Chuck Nice?

  You're happy with yourself. I'm so happy with myself.

• 12:06Neil deGrasse Tyson?

  I'm so happy with myself today. Yeah.

• 12:08Speaker 03

  This is really interesting. We, we have these little molecular machines, these proteins, and we care about that 3D structure and how they work for two reasons. One, we want to understand, you know, how our cells work, because if something goes wrong with that, which is the case for disease.

• 12:24Chuck Nice?

  Right.

• 12:24Speaker 03

  Then we want to understand, okay, where can we, or where do we actually need to go in and start fixing that?

• 12:28Chuck Nice?

  Right. Or how we can stop it from actually going wrong in the first place.

• 12:31Speaker 03

  Exactly, exactly. So that, that's one thing. And then when we think about, okay, how can we go and fix that? What, what we're actually saying when we're doing drug design, we're saying, can we create another molecule that will come into the cell and actually start modulating these molecular machines? It's going, this, this drug molecule is going to actually attach to this protein over here, and that's going to cause this protein to change shape, for example, and so it won't operate how it normally does. Or, and so we stop that protein working, or we make it work better. These are the sort of things we do in drug.

• 13:01Chuck Nice?

  That kind of reminds me of messenger RNA.

• 13:03Neil deGrasse Tyson?

  Vaccines that we developed for COVID.

• 13:06Speaker 03

  Yeah, you know, there's so many different types of molecular mechanisms that we take advantage for for drug design. Wow.

• 13:23Speaker 04

  I'm Ali Khan Hemraj and I support StarTalk on Patreon. This is StarTalk with Neil deGrasse Tyson.

• 13:38Gary O'Reilly?

  Are the folding proteins generally following a set path in the way that they do fold? And you're able to map them and when they misfold, that's when you're able to flag that up or have I just reinvented something or talked rubbish?

• 13:52Speaker 03

  That'd be cool. No, no, you're you're you're on to something. So. That'd be great. So, you know, like the, I mean, the amazing thing is that we can actually, turns out, predict how these proteins fold. So they are.

• 14:04Gary O'Reilly?

  So you're modeling that.

• 14:04Speaker 03

  Yeah, we're modeling that with deep learning, with neural networks. That's what AlphaFold, you know, and all its generations are all about. And that means that we can actually just take in a sequence of amino acids, knowing nothing about this protein before, and then get out the 3D structure. And normally, this would take people months, if not years, to work out this 3D structure.

• 14:25Neil deGrasse Tyson?

  So how is it that AlphaFold knows how a large molecule wants to fold?

• 14:34Speaker 03

  Again.

• 14:35Neil deGrasse Tyson?

  It's got to know that in some way.

• 14:37Speaker 03

  It's learnt this from a few hundred thousand examples. So chemists, biochemists, over the last 50 years, they've been working out these protein structures by hand. They've been literally synthesizing protein, crystallizing it, then shooting X-rays at this to like, look at the electron scattering. And from that, you can resolve the protein structure. It's a pretty hard process, but people have been doing that.

• 15:02Neil deGrasse Tyson?

  That's your way to photograph what the shape of the molecule.

• 15:04Speaker 03

  That's your way to photograph it in reality.

• 15:06Neil deGrasse Tyson?

  With that, with that kind of, it's basically an electron microscope at that level.

• 15:10Speaker 03

  Yeah, similar. It's like electron scattering. Yeah, exactly. And so people have been doing that for the last 50 years and depositing these structures. And now we've taken all of that data and trained a neural network. To go just from the input of what is this molecule description to try and predict all of that data.

• 15:29Chuck Nice?

  Right.

• 15:30Speaker 03

  And the amazing thing is, and this is really remarkable, is that you can then train this on the last fifty years of data, that's a couple of hundred thousand protein and biomolecular systems. But you can apply it seemingly to everything we know about in the protein universe, in the proteome.

• 15:47Chuck Nice?

  Well, it has the...

• 15:48Speaker 03

  Proteome?

• 15:49Chuck Nice?

  The proteome.

• 15:50Speaker 03

  Oh, we like that. Proteome.

• 15:52Chuck Nice?

  Proteome.

• 15:53Gary O'Reilly?

  So how accurate is AlphaFold, an AlphaFold with on the third iteration with its predictions? Because AI has been around a little while, as you've already said, and you're not the only AI tool that's out there, but how accurate is this particular tool?

• 16:07Speaker 03

  Yes, so AlphaFold 2 was that big jump where we started to get experimental level accuracy for just proteins, and that's what won the Nobel Prize in Chemistry.

• 16:19Gary O'Reilly?

  So you balanced it off against empirical experimental...

• 16:21Speaker 03

  The benchmark is doing the real lab work itself. So AlphaFold 2 reached that level. Now AlphaFold 3 expands from just proteins to incorporate other biomolecular types. So proteins with other proteins, proteins with DNA, with RNA, with what's called small molecules, which are the drugs...

• 16:39Neil deGrasse Tyson?

  They go to the neighborhood, they start mixing all that up.

• 16:40Gary O'Reilly?

  Or maybe not the neighborhood, maybe that neighborhood gets a little bit of an upgrade.

• 16:45Chuck Nice?

  No, that's when you make the superhuman. So everybody thinks it's gonna be the Terminator. It's not gonna be the Terminator, it's gonna be the superhuman, and then they're gonna be like us, and they're gonna look down on us and go, you know, why do we need you guys? And that's it. So anyway, you're able to predict these, and have you actually taken any of the modeled predictions and made the proteins?

• 17:10Speaker 03

  Yeah.

• 17:11Neil deGrasse Tyson?

  Or tell us where you expect these to lead to new and innovative drugs, because otherwise it's just a puzzle exercise.

• 17:19Chuck Nice?

  Yeah, it's a great Lego set.

• 17:21Gary O'Reilly?

  We want the guests to enjoy this.

• 17:26Chuck Nice?

  It's like, oh my god, how much that Lego set costs? Only ten billion dollars.

• 17:37Chuck Nice?

  Sorry, go ahead.

• 17:38Speaker 03

  Yeah, yeah, so if you take a particular disease and we think that we can actually, you know, solve this disease by modulating a particular protein.

• 17:48Chuck Nice?

  Mm hmm.

• 17:49Speaker 03

  The question is how we do that. So we design a drug molecule and we want it to fit to this protein in a certain way.

• 17:55Chuck Nice?

  Oh.

• 17:56Speaker 03

  And so this is where traditionally you would have to actually either just guess.

• 18:00Chuck Nice?

  Right.

• 18:00Speaker 03

  Or go into the lab.

• 18:02Chuck Nice?

  And create, crystallize each one of those combinations and then photograph them and see if it worked. But now you can model it and the AI can do a thousand of those in like a minute.

• 18:14Gary O'Reilly?

  Isn't this what you call the target proteins?

• 18:16Chuck Nice?

  Wow.

• 18:17Gary O'Reilly?

  So if you know you've got a certain target protein, do you not then run that against a list of drugs and think, yeah, this one, drug A works better with this, or maybe it's drug D, or whichever letter of the alphabet you're on. And now we become the sort of detective and has this Alpha Fold 3 produced how many clues and how many answers? Or are we still grappling with?

• 18:39Chuck Nice?

  Yeah, instead of trying to figure out the drug, the AI actually figures out the drug for you.

• 18:43Gary O'Reilly?

  Drug discovery.

• 18:45Speaker 03

  Well, exactly. You know, if you let the guy speak, we both hear the two of you.

• 18:49Chuck Nice?

  Okay, we're figuring out this whole industry ourselves, Gary.

• 18:52Gary O'Reilly?

  Yes.

• 18:53Speaker 03

  Yeah, you know, this is exactly where it's going. So

• 18:57Gary O'Reilly?

  Right.

• 18:57Speaker 03

  we can start actually rationally designing these drugs.

• 19:02Gary O'Reilly?

  Right.

• 19:02Speaker 03

  Traditionally, you would take, you know, a million random molecules and you would just throw them at these proteins and see what sticks. And that's how so many drugs have been created historically. You go back further and you're sifting through mud to find these sort of molecules.

• 19:17Gary O'Reilly?

  Is this why there's been such a low percentage of success rates with the sort of drugs for whatever the problem is?

• 19:25Speaker 03

  That's part of it because, you know, we don't necessarily understand how these molecules are working. But with something like Alpha Fold 3, you can, you know, put the molecule, put the target protein into the system, into the neural network, and you get out the 3D structure. And as a chemist, you can start to understand, okay, how is this small molecule drug modulating this protein? Now, still, the problem is, well, how do you find that small molecule in the first place that's going to be good for this protein? You know, it's estimated there's like 10 to the power of 60 possible drug-like molecules out there. That's, you know, 10 with 60 zeros. So even if

• 20:02Neil deGrasse Tyson?

  People know what ten to the sixty is.

• 20:04Speaker 03

  Yeah, yeah, yeah. So.

• 20:06Gary O'Reilly?

  Don't be rude.

• 20:09Speaker 03

  Even if you had the perfect AlphaFold, you'd have to run that across ten to the power sixty molecules, which is just computationally impossible. It's unfeasible.

• 20:19Gary O'Reilly?

  Right.

• 20:19Neil deGrasse Tyson?

  Until quantum computing.

• 20:20Speaker 03

  Until, yeah. And so then what we need is something that we call a generative model, or an agent, which is able to actually search through that space, understand that entire molecular space, and come up with molecule designs for you.

• 20:35Neil deGrasse Tyson?

  Oh, because the ten to the sixty is if you just did it randomly.

• 20:38Speaker 03

  Right, right.

• 20:38Neil deGrasse Tyson?

  Exactly. Right, right. That's just throwing it at anything. Right, right. If you don't do it randomly, then you can.

• 20:43Speaker 03

  But randomly is the state of the art method. That's how people do it.

• 20:48Neil deGrasse Tyson?

  It's how people currently do it.

• 20:49Speaker 03

  It's how people currently do it.

• 20:50Gary O'Reilly?

  Oh, cool.

• 20:51Neil deGrasse Tyson?

  Well, he called it state of the art. You're the state of the art. Right, thank you. Let's use the word properly here.

• 20:56Gary O'Reilly?

  So what if the protein turns left when you've mapped it to turn right? Is that when we have issues that even AlphaFold 3 has a problem with?

• 21:06Speaker 03

  Exactly. These are not perfect models. At the end of the day, they're very, very accurate, but they will make some mistakes. So you still do currently need to go into the lab occasionally, but the amount of lab work you have to do is so much less.

• 21:20Gary O'Reilly?

  Right.

• 21:20Speaker 03

  And often you can find the area of molecular space where these models work really, really well. And we then go out to the lab later down the line. We crystallize these things and we see, yeah, like this is a perfect mapping of what the model predicted.

• 21:35Neil deGrasse Tyson?

  So back to an earlier point.

• 21:37Gary O'Reilly?

  Yeah.

• 21:37Neil deGrasse Tyson?

  In the old days, like last month, you, the pharmaceutical companies, Big Pharma, would spend millions, maybe not quite a billion, hundreds of millions of dollars developing a drug. We think that, holding aside what might be abuses of pricing, the fact that there's some truth to this first pill.

• 22:04Neil deGrasse Tyson?

  Costs fifty million dollars. The second pill costs ten cents.

• 22:08Gary O'Reilly?

  Hmm.

• 22:08Neil deGrasse Tyson?

  Because they had to research to get the formula for that first pill. If you have narrowed the search space, then the cost of developing that first pill can be manifold smaller.

• 22:21Speaker 03

  It costs, on average, three billion dollars to create a new drug.

• 22:25Chuck Nice?

  Wow.

• 22:25Speaker 03

  That's, that's, that's, that's on average.

• 22:27Gary O'Reilly?

  Yeah.

• 22:28Speaker 03

  So, yeah, there's a

• 22:28Neil deGrasse Tyson?

  That was low, when I said a hundred million dollars.

• 22:30Speaker 03

  You were lowballing.

• 22:31Neil deGrasse Tyson?

  Lowballing, okay.

• 22:32Speaker 03

  Um, so this is a massive opportunity to like, completely change just like the cost, the speed.

• 22:37Neil deGrasse Tyson?

  The business model.

• 22:38Speaker 03

  The efficacy, and the business model as we do that.

• 22:39Chuck Nice?

  But is it proprietary? So here's my real, because here's where you would revolutionize. So if I come up with it and I'm, I'm company A, right? It's mine and I get to determine everything. If you're an AI company and you're just doing this, okay, so that you can sell it, then it's yours. Which one will be, will make prices lower for the consumer?

• 23:01Speaker 03

  Our goal is to, is to like really redefine this, this way you do drug design. So it becomes so much cheaper. We have so much more abundance of potential drugs and chemical matter that it really does change the business model. It changes the economics of the space.

• 23:16Chuck Nice?

  So you, you can actually revolutionize the, the cost of making drugs?

• 23:21Speaker 03

  Yeah, yeah, that's, that's where we're going.

• 23:23Chuck Nice?

  Okay, all right. I'm satisfied.

• 23:25Gary O'Reilly?

  Is one of the next steps with AlphaFold, whichever it's through, or maybe the next iteration or so, going to investigate why and what drives the misfolding of a protein? So as you can kind of get ahead of even the story of that happening?

• 23:40Chuck Nice?

  Wow.

• 23:41Speaker 03

  So, so actually, the misfolding of a protein is another thing. That, that's what causes some types of disease.

• 23:48Gary O'Reilly?

  Yeah.

• 23:48Speaker 03

  Where you'll have a genetic mutation, a mutation in your DNA which will change a particular amino acid in that protein. And so it doesn't fold the normal way it should fold.

• 23:57Gary O'Reilly?

  Right.

• 23:57Speaker 03

  And so it doesn't function as, as it normally should as a molecular machine. And so things like AlphaFold can help us understand what are those mutations that cause misfolding? And they're called misense mutations.

• 24:11Gary O'Reilly?

  Right.

• 24:11Speaker 03

  And then, you know, these could be potential drug targets. So we could, you know, think about molecules that could mitigate against that.

• 24:17Neil deGrasse Tyson?

  If I understand correctly, if you look at the, the PDR is this thick. And

• 24:22Gary O'Reilly?

  What's P D R?

• 24:22Neil deGrasse Tyson?

  The Physician's Desk Reference.

• 24:24Gary O'Reilly?

  Thank you.

• 24:24Neil deGrasse Tyson?

  Uh, it's this thick. So it's the size of an old style Manhattan phone book. Okay, it's very thick, multiple inches across. And it's chock full of existing medicines available to the doctor to prescribe. Is it true that 100% of those medicines are interacting with the patient chemically rather than through protein folding? So that if that's the case, does that mean that where proteins misfold, we can't combat it with any kind of folding algorithm? We just prescribe chemistry for your body to handle the impact of that? Is that, did I say any of that right?

• 25:08Speaker 03

  I think that, that, that is the majority of drugs. They are, yeah, they are chemicals that we take. We are the, we, we, you know.

• 25:14Neil deGrasse Tyson?

  The chemical's not gonna fix the fold. It's gonna treat the symptoms of the, of the misfolding that happens.

• 25:19Speaker 03

  We're not, we're not changing the mutations of the proteins. That, that could be something like gene therapy.

• 25:23Neil deGrasse Tyson?

  Mm hmm.

• 25:24Speaker 03

  Um, but these are, these are chemicals that come in and will attach themselves to these proteins and somehow mitigate the, you know, something like a misfold or it'll change an interface, change how these molecular machines work.

• 25:36Gary O'Reilly?

  Hmm, that's wild. Is there a particular disease isomorphic labs are focusing upon right now? Or is this a more of a broad spectrum? Let's do, let's, let's go for proteins and cherry pick out certain things or we'll be really looking at one particular?

• 25:50Speaker 03

  The technology we're creating, it's really, really general. We wanna be able to apply this drug design engine

• 25:55Gary O'Reilly?

  Alright.

• 25:55Speaker 03

  on any protein, any target, any disease area that comes our way. Now, saying that, you know, practically as a company and, and you wanna focus on a particular area, we're focusing at the moment on, you know, a lot on cancer and a lot on immunology.

• 26:10Gary O'Reilly?

  Alright.

• 26:11Neil deGrasse Tyson?

  Two biggies.

• 26:12Chuck Nice?

  Two biggies.

• 26:13Neil deGrasse Tyson?

  Yeah, yeah.

• 26:13Chuck Nice?

  And the two that probably lend themselves best to what you're trying to accomplish, actually.

• 26:17Gary O'Reilly?

  Okay, the question everyone's gonna want me to ask right now that's watching this and listening to this is, how are you getting on?

• 26:25Speaker 03

  Would, I mean, i- i- it's going really well, to be honest. We're seeing these algorithms actually, you know, change the way that we're able to do drug design. We're able to discover, you know, completely novel chemical matter. Again, some of these targets that, you know, people have been working on for, you know, even over a decade. It's really, really hard stuff, making amazing progress. You know, still, still really early in the company, but it's super exciting.

• 26:48Chuck Nice?

  And have you sent anything to be photographed yet?

• 26:52Speaker 03

  We, we, we, we, we sent some things for, for molecular photographs, yeah.

• 26:55Chuck Nice?

  Okay. Yeah, yeah. I know you're not allowed to talk about it. I know. I get it.

• 26:59Speaker 03

  But we're all very happy.

• 27:00Chuck Nice?

  Okay. There you go. Listen, I'm, I'm with you. I'm picking up what you're putting down. That's cool.

• 27:05Neil deGrasse Tyson?

  Yeah, but so if you, if you, I see this work as fundamental research so that you publish a result, you publish the image. As they published the image of the DNA molecule to know that it was a double helix.

• 27:18Chuck Nice?

  Exactly.

• 27:18Neil deGrasse Tyson?

  That becomes public knowledge at that point. So someone with tools, access to AlphaFold 3, would any company have access to this once you have published the blueprint for it?

• 27:29Speaker 03

  In, in drug design, often these blueprints come out in the patents. So when, when you're, when you're going to go into clinical trial, you need to patent these molecules. And in those patents, you'll have a lot of data around the molecules, the formula, how they work.

• 27:41Chuck Nice?

  That's what I was talking about earlier.

• 27:42Neil deGrasse Tyson?

  Yeah, yeah, yeah. Okay.

• 27:43Chuck Nice?

  Yeah.

• 27:44Neil deGrasse Tyson?

  All right. So the immune system, the, the cancer, these are leading causes of maladies in this world. What of the genetic disorders that affect one in a hundred thousand people?

• 28:02Chuck Nice?

  Wow.

• 28:02Neil deGrasse Tyson?

  You, you bring them together, there's enough of them, you know, they'll fill a stadium in the world, but that's so uncommon as to not really trigger anybody's interest.

• 28:13Chuck Nice?

  It's also not profitable.

• 28:16Neil deGrasse Tyson?

  Well, yeah.

• 28:16Chuck Nice?

  Because you don't have, you don't have enough of a market there to sell the drug.

• 28:19Neil deGrasse Tyson?

  Right.

• 28:19Speaker 03

  I mean, you know, it's exactly that, that point, Chuck. It's, um, traditionally, it might not be that attractive commercially to go after...

• 28:28Speaker 03

  You know, very small patient populations.

• 28:30Chuck Nice?

  Hmm.

• 28:30Speaker 03

  But in a world where it's so much cheaper, so much easier to get to these drug molecules, then that opens up like all of this space. Okay.

• 28:39Neil deGrasse Tyson?

  Like the cheaper it is, the

• 28:40Chuck Nice?

  Right.

• 28:40Neil deGrasse Tyson?

  the easier you can justify going down that

• 28:44Chuck Nice?

  Right down the risk list.

• 28:45Speaker 03

  And this is a big guiding star for us. Like that's, this is why we're doing this.

• 28:47Chuck Nice?

  That's beautiful.

• 28:48Neil deGrasse Tyson?

  I see what you did there.

• 28:49Chuck Nice?

  Guiding star, yeah. You know, it'd be great though.

• 28:51Gary O'Reilly?

  He's picked up the environment. He's not silly. Why is chief AI officer?

• 28:55Chuck Nice?

  Yeah. I'm interested to how active the company is in shaping policy around what you're doing, because there's going to be a great deal of legislative policy that is going to be tied to what you're doing. All of the patent implications, there's going to be, you know, research implications. There's going to be a lot of things tied to this.

• 29:17Speaker 03

  Yeah, yeah. I mean, we, we've been talking in this conversation about drug design, but then, once you've designed the drug, you've got to go into patients in clinical trials.

• 29:24Chuck Nice?

  Right.

• 29:24Speaker 03

  And that's a really long process.

• 29:26Gary O'Reilly?

  Yeah.

• 29:27Neil deGrasse Tyson?

  That's why we have mice.

• 29:28Speaker 03

  But even these mice models, they're not actually very predictive. Like you do all these studies in mice and then they don't, you know, it doesn't translate into success in people.

• 29:36Chuck Nice?

  Right.

• 29:36Gary O'Reilly?

  You've got to go up the evolutionary scale and then get to the human bit.

• 29:39Speaker 03

  Yeah, exactly. And so there's, you know, you can imagine a world where we can design loads of new drugs. We've got to be changing the way that we're doing clinical trials, you know, how we can actually get these drugs to patients who really, really need them in a timely manner. Um, so I think there's a lot to be done and like rethought there.

• 29:55Gary O'Reilly?

  Is the ultimate goal for Alpha Fold and I think medical science as a whole, to be able to bespoke medication for you as the individual, rather than the broader spectrum medication that we find ourselves with all the side effects. So are you able to then design a drug or a medication that has zero side effects and works exactly for me?

• 30:16Speaker 03

  This is the goal, right? This is, this is what we're shooting for. You know, imagine a world where we can sequence your particular cancer mutations.

• 30:25Chuck Nice?

  Right.

• 30:25Speaker 03

  And then based on those, your individual mutations, be generating specific drugs for you.

• 30:31Chuck Nice?

  Right.

• 30:32Speaker 03

  That even these are like, you know, 3D printed or something around the corner.

• 30:35Chuck Nice?

  Okay. Yeah, this is, um, I mean,

• 30:37Speaker 03

  We're not there yet.

• 30:38Chuck Nice?

  We're in the very nascent stages of that right now with immunotherapy for cancer treatments and.

• 30:43Neil deGrasse Tyson?

  But how many of these yet to be cured diseases lend themselves to solutions that involve protein folding? And how many are just? Plain old, old fashioned chemistry.

• 30:55Speaker 03

  Proteins make up like pretty much all of our molecular machinery. So there's, there's a class of disease which is due to misfolding. But then there's many, many other diseases which are due to, for example, a protein not being expressed properly.

• 31:08Neil deGrasse Tyson?

  Right.

• 31:08Speaker 03

  Or, you know, a, a, a cell going wrong in a certain.

• 31:12Neil deGrasse Tyson?

  If I have a bacterial infection, I give myself antibacterial chemicals and then I'm done. Do I need you for that?

• 31:20Speaker 03

  But those chemicals are interacting with the proteins in the bacteria.

• 31:24Neil deGrasse Tyson?

  Okay.

• 31:25Speaker 03

  So proteins are the fundamental machinery and the chemicals which are drugs are modulating those proteins. Whether it's like in our cells, in bacteria.

• 31:33Neil deGrasse Tyson?

  All right.

• 31:33Chuck Nice?

  So it's basically everything you're talking about is, is all happening on the cellular level.

• 31:38Speaker 03

  Yeah.

• 31:38Neil deGrasse Tyson?

  If what you're describing is happening inside of cells, proteins doing their thing, their 3D jigsaw puzzle. And you have a solution for that, a remedy, you have to get your remedy inside the cell to interact with that folding.

• 31:52Chuck Nice?

  The delivery.

• 31:53Neil deGrasse Tyson?

  And how do you do that other than through like a Trojan horse virus or something? Cause viruses get in there pretty on command.

• 32:02Speaker 03

  Yeah. Well, if you think about the drugs that you take as pills, drug, drug design is really hard because it's not just about targeting these proteins. We've got to get them to the right place.

• 32:10Chuck Nice?

  Mm hmm.

• 32:10Speaker 03

  We might want a pill that you take. So you take this pill. It's got to be absorbed by the body.

• 32:15Chuck Nice?

  Right.

• 32:15Speaker 03

  So it's got to be soluble.

• 32:16Chuck Nice?

  Right.

• 32:16Speaker 03

  It's got to go through the gut wall. It's got to go through the bloodstream to the particular tissue type, the cell type you care about. Then it's got to go through the cell membrane to be able to actually target maybe a target which is within the cell. So you need all of these properties in a single molecule. So we're actually designing these molecules, not just to hit the protein, but also to be soluble, to be cell permeable. There's so many different factors and then you don't want this molecule to be toxic. So you want it to hit the target of interest, but not hit anything else.

• 32:46Neil deGrasse Tyson?

  Right. So I can see how a molecule gets through the cell wall.

• 32:49Speaker 03

  Yeah.

• 32:49Neil deGrasse Tyson?

  A, a simple molecule, but a full up. Protein, red-blooded protein, how's that getting through the cell wall?

• 32:55Speaker 03

  Exactly, there's different types of drugs. Some are what we call small molecules, things you could take as pills. Others are made from proteins. They're the often things that you would inject directly inside.

• 33:07Gary O'Reilly?

  Right.

• 33:08Speaker 03

  And some of those might be cell permeable, they'd be things like peptides. But often these protein-based drugs, things like antibodies, they're injected, but they don't go in the cell. They're just interacting with proteins on the surface of cells. So you don't need that permeability. So it really depends on what your target is and how do you want people to be able to take that drug? Sometimes a pill is the best thing, but actually sometimes injecting is the best thing.

• 33:32Gary O'Reilly?

  Yes.

• 33:32Neil deGrasse Tyson?

  And remind me what a peptide is?

• 33:34Speaker 03

  A peptide is a really small protein. So you've got full-blown proteins, which are these big molecular machines. And then you've got small proteins made up of, you know, five to 50 amino acids.

• 33:46Neil deGrasse Tyson?

  Those are peptides.

• 33:47Speaker 03

  Those are peptides, they're smaller, so sometimes in some configurations, they can get through the cell wall.

• 34:11Gary O'Reilly?

  So with this computer science, are we upending chemistry as we've known it? And are we going to find it kind of moving on?

• 34:20Neil deGrasse Tyson?

  With AI?

• 34:20Gary O'Reilly?

  Yeah. With the AI, is it then sort of moving into the other sciences? Are we going to just see it stick in one particular area?

• 34:28Speaker 03

  Chemistry is always going to exist. It's like, to me, it's like any field of science at the moment. Doing science like chemistry without maths, you wouldn't think about that now. And it's going to be the same thing with AI, it already is in my mind. You just wouldn't do chemistry without AI, you wouldn't do biology without AI. It's just that fundamental tool that allows us to understand the world better.

• 34:49Chuck Nice?

  So chemists will not one day be like coal miners? Just like, I remember my grandpapa used to go into the lab.

• 35:01Gary O'Reilly?

  So going back to an issue Chuck was mentioning.

• 35:03Neil deGrasse Tyson?

  He come home smelling like chemicals.

• 35:08Gary O'Reilly?

  Who's actually going to be able to access AlphaFold 3 or AIs of this iteration? Is it exclusively isomorphic labs or this comes out in license?

• 35:17Neil deGrasse Tyson?

  I want a home kit.

• 35:19Gary O'Reilly?

  That's it, exactly.

• 35:20Neil deGrasse Tyson?

  Right, right, right. My DNA goes in.

• 35:21Gary O'Reilly?

  Kitchen sink.

• 35:22Neil deGrasse Tyson?

  There's an AlphaFold going on. Out comes a pill and I take it and I don't even need you at that point.

• 35:26Chuck Nice?

  That would be so cool. You just finger prick or like put your finger on a sensor or something and it figures it all out.

• 35:33Neil deGrasse Tyson?

  Make your own pill in the, at home.

• 35:34Speaker 03

  Yeah, yeah. Look up the Theranos story for that one.

• 35:39Chuck Nice?

  Wow, that's the woman that went to prison, right?

• 35:42Speaker 03

  But no, you, you can access AlphaFold. So if you search for AlphaFold server, um, there's a whole web-based system where you can, you can fold proteins there for, you know, academic uses. It's, it's really cool. So you can just put your system in, get the 3D fold out from AlphaFold 3, download it, yeah.

• 35:58Chuck Nice?

  Oh, cool, man.

• 35:59Gary O'Reilly?

  I mean, how far away are we from modeling an entire human being, which I suppose touches on to your fears?

• 36:04Neil deGrasse Tyson?

  Modeling or creating?

• 36:05Gary O'Reilly?

  Either or.

• 36:06Chuck Nice?

  Once you model, the next step is creating.

• 36:09Neil deGrasse Tyson?

  Yeah, that's true.

• 36:10Chuck Nice?

  That's all there is to it.

• 36:11Neil deGrasse Tyson?

  That's true.

• 36:12Speaker 03

  That, that's the dream. We kind of need to work up the scales here. So, you know, we, we, we can model how two atoms interact. We can write down those equations. We can simulate small atomic systems. With things like AlphaFold, we get into bigger atomic systems, things on the scale of like multiple proteins. Now we've got very, very accurate AlphaFold 3. Maybe we can actually bootstrap off that to get to more sort of even bigger systems. What we call pathways, how all of these things interact.

• 36:39Neil deGrasse Tyson?

  It's only just now clicking within me because you can look up in a book the tables of, um, action potentials for the interactions of, of atoms and molecules. And so you'd have a very good sense of which molecules will combine. Is it exothermic? Is it endothermic? But these are atoms and molecules. And as powerful and as convenient as that is, that's just the first rung in this ascending ladder of complexity that you are gaining control over.

• 37:11Speaker 03

  Yeah, and there's, there's trillions of atoms within a single cell, let alone the whole human body.

• 37:17Chuck Nice?

  Right.

• 37:18Speaker 03

  It's just unfeasible to simulate the whole thing, but. What you can do is you can, you know, we do have good measurement techniques at different levels of scale, so we can measure things like protein folding. We can measure the amount of protein within a cell. We can measure the number of cells of certain type within a tissue. And so we have these like- The bigger it is, the easier it would be to measure. So we have these little windows into this sort of microscopic world. And then we can use AI to sort of fill in the gaps and bootstrap off the stuff we can do well, the atomic level, and start building up that scale of modeling, if that makes sense. We can rebuild it.

• 37:52Chuck Nice?

  I read this article like God knows how long ago.

• 37:55Speaker 03

  God, how long did Chuck read the article?

• 37:57Chuck Nice?

  God, because you know God. Anyway, it was talking about when a fertilized cell starts to proliferate and become a person. And basically what it determined, what the scientists determined at that point, and this is many years ago, is that the only way they could describe it is there's a bunch of noise. Like, there's just a bunch of noise. We can't really see anything. We can't make sense of any of it, because it's just basically, if we were to look at it as data, it would just be noise. Are you able to pierce that veil and see into that?

• 38:33Speaker 03

  I mean, we haven't been looking at that specific thing.

• 38:36Chuck Nice?

  Okay.

• 38:36Speaker 03

  But this is where you start to understand more about, you know, a really granular scale. And then you, you can integrate that and create these sort of, I don't know, coarser measurements and coarser predictions. This is what we do in lots of areas of science, right? We, we don't simulate the whole universe at the atomic scale, but we, we find these rules of thumb or ways to describe sort of broader collections of molecules. And that's what we can start to build up and actually learn with these neural networks.

• 39:02Chuck Nice?

  Cool.

• 39:03Gary O'Reilly?

  So, question on behalf of Chuck. Could Alpha Fold discover a hallucinogenic that could make him see God? Or any other deity or being?

• 39:14Chuck Nice?

  Thank you for asking. Welcome. I would like a very real answer, please.

• 39:21Speaker 03

  You can go on the AlphaFold server and try that out.

• 39:23Chuck Nice?

  Okay. All right, invitation.

• 39:25Speaker 03

  Hey, listen, I'm all about it.

• 39:27Gary O'Reilly?

  So, we've looked in previous shows, talking to biomedical engineering, and if we are to travel off-world and deep space, we are probably going to need different upgrades for us to be able to do that. Are we going to be able to, with AlphaFold or AI like this, be able to upgrade ourselves to make this sort of deep-space travel?

• 39:52Neil deGrasse Tyson?

  Or upgrade ourselves for anything?

• 39:54Gary O'Reilly?

  Yeah, I mean, this is a sci-fi, you know, the...

• 39:57Speaker 03

  Yeah, now we're sort of beyond, you know, solving disease into like, actually, can we enhance ourselves?

• 40:02Gary O'Reilly?

  Right, yeah.

• 40:03Speaker 03

  I don't know, I think like, there's probably potential, right, to think about creating chemical matter that we can take or ingest that...

• 40:11Chuck Nice?

  I mean, aging alone would be a huge application for this.

• 40:16Speaker 03

  I mean, there's crazy research on aging.

• 40:17Chuck Nice?

  Because aging is basically cellular degeneration, and if you're able to, on a molecular level, kind of restart that process or jump-start it or, you know, boost it.

• 40:27Speaker 03

  Aging is an interesting one. You know, this is a really nascent area of research, where people are just starting to work out what are some of the factors that reverse the age of cells. There are these things called Yamanaka factors.

• 40:38Gary O'Reilly?

  Yes.

• 40:39Speaker 03

  And there's even potential that people are finding of creating molecules that stabilize particular protein... Yamanaka factors are proteins, they're transcription factors that read DNA. Can stabilize these things, maybe that is what reverses some of the age of cells. This is super nascent.

• 40:55Neil deGrasse Tyson?

  So what is the connection between wanting to modify a genome and your ability to fold proteins to interact with our physiology? I ask that because I'm reminded, there was a scene in the film Gattaca, where they didn't manipulate your genome, but they selected your pre-existing genome for certain properties.

• 41:14Chuck Nice?

  Oh, yeah.

• 41:15Neil deGrasse Tyson?

  And there's a person giving a piano recital, and it was very rich sound, I mean, it was beautiful. And then the camera came around to the front, and the person had 12 fingers.

• 41:24Chuck Nice?

  That's right.

• 41:24Neil deGrasse Tyson?

  And... Bread for that.

• 41:27Chuck Nice?

  Yes.

• 41:28Neil deGrasse Tyson?

  Right, you get two extra notes for every.

• 41:30Chuck Nice?

  For everything, right.

• 41:31Neil deGrasse Tyson?

  Everything going on.

• 41:32Chuck Nice?

  She could only play the stuff, like, nobody could play what she could play.

• 41:34Neil deGrasse Tyson?

  Nobody could play with it.

• 41:35Chuck Nice?

  Nobody could play with it.

• 41:36Neil deGrasse Tyson?

  So, this would be modifying not to go into space, necessarily.

• 41:39Chuck Nice?

  Yes.

• 41:40Neil deGrasse Tyson?

  But just to sort of enrich the diversity of the human species.

• 41:44Speaker 03

  We're, we're not doing genetic modification.

• 41:46Neil deGrasse Tyson?

  So he says.

• 41:49Gary O'Reilly?

  He's English, you must trust him.

• 41:51Speaker 03

  To be honest.

• 41:52Gary O'Reilly?

  Thank you.

• 41:53Chuck Nice?

  Thank you. Well done.

• 41:55Neil deGrasse Tyson?

  Well, well played. What, so, but, can you? Is it the same thing?

• 42:00Speaker 03

  Some particular types of drugs are, you know, things that would manipulate your genome.

• 42:06Chuck Nice?

  Um, yeah.

• 42:07Speaker 03

  That's how people, you know, start to target some diseases. This is not the class of drugs we're working on. When we think about the big ambition of solving a disease, maybe this is something that we need to be, um, doing over time as, as we wanna really crack, like, the whole spectrum of disease.

• 42:21Chuck Nice?

  Right.

• 42:21Gary O'Reilly?

  Can, is it even possible to consider that without considering the whole area itself as, all bleeds in together at some point?

• 42:29Speaker 03

  Yeah, I mean, we need to understand the genome and, and all the effect, you know, how, how you, how changing a particular base pair on your DNA is gonna change, you know, what proteins are expressed or in what abundance and how that, all the knock-on effect on the pathways. You really want this, like, basically this virtual cell to be able to manipulate this cell on a computer.

• 42:50Chuck Nice?

  Right.

• 42:50Speaker 03

  To do experiments there.

• 42:51Neil deGrasse Tyson?

  Here, here's what I want you to do.

• 42:53Chuck Nice?

  It's like your cell template that you.

• 42:54Neil deGrasse Tyson?

  Talking about prioritizing, and I'm not asking much, cause it already happens in the animal kingdom. You know, for so long, decades even, centuries, we imagined ourselves at the top of some evolutionary.

• 43:06Chuck Nice?

  Yeah. Triangle, where the apex of the triangle.

• 43:09Gary O'Reilly?

  Without any arrogance whatsoever, yeah.

• 43:11Neil deGrasse Tyson?

  And, alright, yet, a newt can regenerate a limb.

• 43:16Chuck Nice?

  Mm-hmm.

• 43:16Neil deGrasse Tyson?

  And we can't.

• 43:17Chuck Nice?

  Yeah.

• 43:18Neil deGrasse Tyson?

  And so, it seems to, and they're vertebrates. So, it seems to me there ought to be some way to extract from animals that do things that we could benefit from, and then make that a priority. So, people, especially veterans who've lost limbs.

• 43:33Chuck Nice?

  Right.

• 43:34Neil deGrasse Tyson?

  In, in, in conflict.

• 43:35Chuck Nice?

  Yep. So. Or, or even geckos with their sticky hands, like, maybe, um, I could be Spider-Man one day.

• 43:41Gary O'Reilly?

  Well, so, all gonna end up as.

• 43:42Neil deGrasse Tyson?

  Let's prioritize that, Chuck.

• 43:45Gary O'Reilly?

  Or become superheroes.

• 43:48Neil deGrasse Tyson?

  So the regeneration of limbs, that's gotta be a protein thing going on in there, isn't it?

• 43:54Speaker 03

  Yeah, I mean, you know, all of our mechanisms are protein, same for nukes as well. So there is some mechanism there. I don't know what it is.

• 44:00Neil deGrasse Tyson?

  Okay.

• 44:01Chuck Nice?

  Gotcha.

• 44:01Neil deGrasse Tyson?

  But that would be a mechanism to emulate, if you could.

• 44:04Chuck Nice?

  If you could, yeah.

• 44:05Neil deGrasse Tyson?

  And then install it into our own physiology.

• 44:08Speaker 03

  It's a big if.

• 44:09Chuck Nice?

  Yeah, that's a lot. How about this? Would you be able to look at drugs that are already here? And there are some drugs that are just not well tolerated. And you'd be able to reconfigure them in such a way that you get the benefit of the drug without the side effects?

• 44:23Speaker 03

  Yeah, exactly. So you often have these first generation of drugs that do something, but they have these side effects. Then there's a big opportunity to understand better how these drugs work. Things like AFOP 3, things like our models that understand toxicity of drugs. Can then allow us to potentially modify these to become better drugs and have less side effects, less toxic effects.

• 44:45Chuck Nice?

  Cool.

• 44:46Gary O'Reilly?

  Wow, that's going back through the medical catalog and reanalyzing, which is exactly what an AI would be perfect for.

• 44:52Chuck Nice?

  Right, exactly. Yeah, you guys are gonna make a lot of money, man.

• 44:55Gary O'Reilly?

  Stop seeing dollar signs and see some.

• 44:58Chuck Nice?

  How do I get a piece of this company? I need to get a piece of this company. You guys are gonna make, I mean, I can't even imagine the amount, the gobs and gobs of money. More money than cells in my body. This is amazing.

• 45:13Neil deGrasse Tyson?

  We're on the doorstep of quantum computing, and I know what impact that would have in my field. In your field, would it make your entire life's work look like it was done on an abacus?

• 45:22Speaker 03

  Yeah, I mean, this is gonna change things. I think open question, how this changes machine learning, like what can AI do with quantum computing? But for chemistry, even near term, there are some real applications of quantum computers for understanding the properties of small molecule drugs. Because actually, some of the things that people do do today with quantum computers is simulate these small chemical systems. Like, we actually, even in the company, we have a quantum simulation team. That are, you know, not using quantum computers, but simulating, you know, the quantum effects of molecules. Now, if you had a quantum computer that could work on that scale, you could use that instead, so.

• 46:02Chuck Nice?

  Wow.

• 46:03Neil deGrasse Tyson?

  I think of so many needs on the frontier of chemistry in modern society. Uh, one of them is, you know, what do we do with all the plastic that's in our environment? That's still there in the ocean. Is there some life form you can create that'll digest the plastic and turn it back into its original molecules? Is, are proteins something that could be applied there? If not in your world, then you're describing an ability more than you're describing a specific solution to a problem. You, you're empowering the chemist in ways never previously imagined.

• 46:39Speaker 03

  Yes, so you can use the capability of, of AlphaFold to understand, you know, understanding structural proteins. People are using this outside of drug, drug design.

• 46:48Neil deGrasse Tyson?

  Exactly, exactly.

• 46:49Speaker 03

  People are using this, for example, to create bacteria that have enzymes that could potentially digest plastics, like you're talking about. You could think about this for engineering, um, you know, more resilient types of crops, these sort of things.

• 47:01Chuck Nice?

  Yeah, so just like AI, this is a platform upon which you can rest the technology of any field, so.

• 47:08Speaker 03

  Yeah, that's the amazing thing about the protein folding problem. Once you start to solve that, you unlock so many new things for a whole broad spectrum of science.

• 47:16Gary O'Reilly?

  There's a lot of downstream benefits, I would say.

• 47:19Neil deGrasse Tyson?

  Okay, last thing.

• 47:20Chuck Nice?

  Here's the last thing. How, how do I, how do I get a piece of this company?

• 47:24Neil deGrasse Tyson?

  Chuck is good dollar times in his eyes. Last thing. La, last thing. What is the worst possible outcome of your work?

• 47:32Chuck Nice?

  Ooh, what a question.

• 47:35Neil deGrasse Tyson?

  What guardrails are necessary as we go forward? Cause any new technology with awesome power comes with awesome responsibility.

• 47:42Speaker 03

  Yeah, I mean, I, I think, um, you know, you have this with AI, you have this, you know, creating new biology or, or, or chemicals. You know, you, you just need to think about how to use this responsibly, like what you're putting out into the world openly versus what you, um, close off, uh, you know, many safety reasons. So I think there's, there's a lot of things to consider there, yeah.

• 48:02Neil deGrasse Tyson?

  Cause famously in one of the Jurassic Park films, they withheld lysine, amino acid, from one of the dinosaurs in case it escaped. It would die because it would need the lysine for its survival. And that was a kind of a, an insurance plan that put in, but life always finds a way.

• 48:26Chuck Nice?

  Yeah. There you go.

• 48:29Neil deGrasse Tyson?

  Anyway, Maxwell Yadeberg. Yeah. Thank you for joining us on StarTalk. We're gonna be watching your company and Chuck wants a piece of it. Yes. I don't know what that means. But anyhow, this is, I'm delighted to just be able to look through your lens at the birth of an entire frontier in human physiology. I mean, what a time this is.

• 48:50Speaker 03

  No, thank you so much. It's been super, super fun to talk.

• 48:52Neil deGrasse Tyson?

  No, thank you. Excellent, excellent. All right, I think we're done here. Mm-hmm. It's been another installment of StarTalk Special Edition. Talking about AI, human physiology, and the future of drugs. Oh, yeah. Gary, good to have you always. Pleasure, Neil. Chuck? Always a pleasure. As always. This has been StarTalk. Neil deGrasse Tyson, your personal Asher physicist. Keep looking up.