Profluent Bio raises $106M to design proteins from scratch with AI — trained on 100 billion proteins and 20 trillion tokens

Bye.

Uh let me tell you about wander.com. Book a wander with inspiring views, hotel grade amenities, dreamy beds, top tier cleaning, and 247 concier service. Our next guest is already in the race room waiting room. We have Ali Madani. Welcome to the show.

How you doing? Thank you so much for taking the time to come chat with us. Uh please introduce yourself, introduce the company, tell us what the news is today.

Sure, absolutely. Uh so my name is Ali. Um I have a PhD in machine learning from uh UC Berkeley. I've been working in the space of biology and AI for almost a decade now. Uh previous to this, I I led a moonshot at Salesforce pioneering language models for biology. Yeah. Um, and what started out as a purely scientific endeavor to develop transformer models for sequence generation has led into ProFlent spec Pro Profolent specifically where our mission is uh to make biology programmable and I'm happy to kind of break that down.

Yeah. Yeah. Uh I I I've seen obviously there's there's a ton of uh just like momentum in the space. AI curing cancer is like a buzzword that a lot of people are throwing around. Um, how are you uh thinking about concretizing what you're actually how you're trying to fit in? Are you a tool? Are you a drug maker? Is it uncertain? Uh, like who are your customers? How how much are you in like a science project world? Like, you know, you could be a nonprofit in another in another era versus like you're ready to commercialize, you're going to market. And not that there's one path that's wrong or the other, but I'd love to know how you're thinking about the business right now.

Totally. Yeah. I think there's a a lot to unpack there. Um I think [clears throat] the meme that came to mind specifically, I don't know if it's a South Park meme or otherwise where it's it starts with like build something and then there's a dot dot dot question mark and make profit and then profit. Yeah. Step one, step one, [laughter] you know, make biology programmable. Step two, bro down with your boys. Step three profit. And

and I think a lot of folks, you know, right now there's an incredible amount of excitement around AI. And it's kind of like step one is make a chat bot, for example, right? and then it's question mark dot dot dot and then solve you know disease or cure cancer specifically whereas what we're actually trying to build here is actually tackle on the disease head-on specifically so what we do is we build language models so the same language models that have enabled GPD2 3 and four and chat GPD specifically these incredible models and algorithms that can learn on sequences what we can feed instead of words in a sentence is actually amino acids that are strung together to form a protein and uh why that's actually important and why making biology programmable. Maybe to take a step back, like people usually shut off their brains when it comes to biology. Yeah. And and when it comes to like rockets landing on a platform in the ocean, we're amazed, right? And that makes sense, right? Like it's these are man-made machines. We can see it. They're incredible. But honestly, biology is not that much different. There are these molecular machines called proteins that enable us to breathe and see. They're responsible for everything in human health and disease. And also they sustain the environment involved in daily daily products like even our detergents to begin with and how let's actually stick to drug discovery in particular. How we've gone about finding these solutions these molecular machines that we utilize day in day out has actually been through random discovery. So you know that that middle school example of Alex al Alexander Fleming coming across penicellin right he had a petri dish they molded for example and then they found the advent of antibiotics and now after you get a cut on your skin for example where bacterial infection happens it's no longer a death sentence right um that actually is not the exception it's the rule in which we've gone about finding life-saving medicines even fast forwarding to today crisper cast 9 was actually found in a Disco yogurt facility where people found these interesting bacteria doing these interesting characteristic had having these interesting characteristics and we've taken a molecule plucked it from nature and then crammed it within human therapeutic applications to actually save lives. And honestly like to put this really in rudimentary terms that's that's kind of absurd. It's almost cavemanlike in terms of our technique our techniques that we have and methods that we have available for us for drug discovery. And what we're trying to do is actually move away from random discovery and finding a needle in the hay stack and relying on nature altogether and using AI to design bespoke medicines from scratch. Um and that's you know like that's our mission to really gain control and mastery over biology and perform bespoke design. So in terms of your question of like where are we with respect to you know is this just a science project or how's the commercialization looking specifically? I would still say we're in early days like the equivalent of GPT eras of like maybe GPT1 or GPD2 but we've already seen incredible amount of traction. So we had this project called open crisper specifically where we took is uh we took these uh these language models trained on gene editing proteins specifically and generated a novel protein from scratch called open crisper one that thousands of people use now in pharma large pharma small biotechs academics and ind industry um uh users and scientists as well and over thousands of people use this over worldwide today and I think that's like it's amazing to actually see us solving problems today that have lead to commercial traction and that we have partners both from uh therapeutics to diagnostics to biommanufacturing even agriculture that are utilizing today.

Can you talk about like how you create feedback loops as a company because you know there's no shortage of people in AI that talk about the uh opportunity of like curing various diseases. Many of them aren't saying that from the standing in an actual lab. You are standing in a lab. that makes me more excited about what you're doing because you're not just kind of, you know, like it there's you're not just saying like, oh, like the next version of the model will we'll just do this like don't worry about it. It's like no, like we're going to run a lot of experiments. Um, but yeah, talking about

yeah, like you know, using AI to uh to to learn and and generate uh you know, potential approaches uh but then actually bringing it into a lab setting.

Absolutely. Yeah. um we operate within a pre-training and post-training paradigm within proteins, similar to NLP and natural language processing as well. So the pre-training step really involves in similar to how we have all of the internet that we can scrape from and can learn these underlying principles and grammar and semantics as to what makes human generated texts. We've actually collected a tremendous amount of data of proteins that have naturally evolved through nature for selective reason selective pressures and evolutionary kind of pressures that have shaped those proteins specifically to make a functional protein. Um, and just to put that into context, um, Alphold 3 was trained around, it was exposed to around two to three billion proteins. Uh, what we've actually trained to date so far at ProFlint is over a 100red billion proteins. And to put that into uh, tokens, that's over 20 trillion tokens. Exactly. [laughter] Um, so there's there's an incredible amount of data for pre-training purposes that we utilize. And then what you see behind me as well is the data that we're doing the assay labels labeled examples meaning actually taking protein sequences and then measuring their function not just in vitro and test tubes and petri dishes but in human cells and relevant cellular contexts and seeing how well they actually perform and we can feed that back into our models. So I think that's you know the future is really an integrated future where you're building frontier AI models and having uh the the the the closed loop specifically with respect to the wet lab which is what what's behind me today um to actually test these and feed them back into our models to get better and better over time. So yeah

well congratulations. I want to ring the gong for you. What's

by the way Gersner and Bezos I mean

how much

potentially the coolest

cap table the cap table [snorts] of the year. How much was the deal?

Yeah, absolutely. Yeah, it's $106 million that we're announcing.

Um, and I I think what's more important what's more important than number are these legendary investors that we have. I mean, Jeff Bezos is a legend. Uh, he's transformed industries. Uh, and I think what's exciting for him and for us as well is that biology is the next frontier for AI specifically that will have tremendous impact. Um, and really honestly is the most important quest in our lifetime. So, we're really excited. so much for

I'm sure you will be back on very soon.

And congratulations on all the progress.

Great. Great to meet you.

We'll talk to you soon.

Talk soon. Have a good one.