How AI is Reinventing Chemistry (From a Trailer Lab to a $32B Partnership)
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How did a backyard trailer lab lead to a $32B partnership?
What if I told you someone built a $270 million chemistry AI that started in a backyard trailer lab, and today that AI is helping redesign products used by billions of people from Tylenol to Neutrogena? Let's talk about the future of materials science.
Welcome, humans, to the Neuron Podcast. I'm Corey Knowles, and joined, as always, by Grant Harvey. How are you doing today, Grant?
Doing good, doing good. Very excited today because we are talking to Nick Tolkien, CEO and co-founder of Albert Invent, an AI platform that is transforming how chemistry gets done at some of the world's biggest companies. Excellent.
Well, Nick, welcome to the Neuron. Great to have you.
Hey, guys. Great to be here, and thanks for having me.
Excellent. Well, I guess to start out, let's talk a little about your origin story. I understand you and Ken started Molecule Corp in a literal trailer lab in the backyard. Is that right?
Yeah, it's a fun story. So yeah, Ken has been in the chemical industry for about 25 years before that. He actually grew up in his dad's paint factory. So the story goes even farther back, and maybe you'll have to get him on the podcast at some point to tell that part of the story. But Yeah, back in 2014, he pulled a trailer into his backyard to start his third material science business. That company, as he said, was called Molecule. And it was really about changing the way that chemistry is invented because he was frustrated that it hadn't really changed in his entire career in the industry. And so it was a great opportunity for me to join him and help him on that mission.
That's awesome. So I'm really excited to talk to you because I feel like right now we've gotten to a point where the AI agents that we know of today, some people can use them well, some people are finding the limits of how useful they are, especially in like workplace, B2B, SaaS, that area. But I feel like AI and science is the untapped kind of like area that is the most exciting where there's the most potential benefit for good. And I'm just like really excited to talk about it from that standpoint, especially for chemistry. So for folks who aren't chemists, when you say Albert is trained on 15 million molecular structures, what does that actually mean? You want to just walk us through that? Yeah.
Yeah, so one of our core beliefs as a company is that you can't just take off-the-shelf generic large language models, gen AI, machine learning, whatever, and start applying it to science. I think if it was that easy, it would have been done a long time ago. And so what you have to do instead is you have to take advantage of the underlying data that exists out there in the world. And there's basically two forms of data that exists out there. There's the publicly available data. So that's the patent landscape, the literature, stuff coming out of academia. And then there's the enterprise data. And our mission at Albert is to help the largest and the biggest enterprises take advantage of both of those sources.
So the public, the private. So when you mentioned 15 million molecules, that's actually coming from the public space. You know, there's there's a lot of publicly available data out there put out by government agencies and academia and the like. And we build foundational models of chemistry with that public data. The problem, frankly, though, and again, the reason it's not that easy, is that generally the data in the public domain is the successes, right? There aren't many papers and patents that are publishing just all the failures. And if you remember back to your science experiments, I assume that most of the experiments you did in the lab and that everyone does, they're failures, right? That's actually where you learn the most.
Yeah. Just taking that data, even with that large number of publicly available experiments, it's not enough to just crack some of the problems that the industry faces today.
Wow. That's really interesting. So your platform is called an end-to-end R&D platform. What does that mean in practice?
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Chapters
5 chapters
1
How did a backyard trailer lab lead to a $32B partnership?
0:00–8:55
2
What is the significance of training AI on 15 million molecular structures?
8:55–9:46
3
Why can't off-the-shelf AI tools like ChatGPT be used for chemistry?
9:46–15:51
4
What does an end-to-end R&D platform look like in practice?
15:51–33:36
5
What challenges do enterprises face when sharing proprietary data?
33:36–40:37
Speakers
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