Lab-in-the-Loop at Compute Speed — Ian Quigley, Leash Bio

Ian Quigley (0:00)

My co-founder Andrew Blevins and I were at Procursion looking at a small molecule on a screen. And then a medicinal chemist walked by, it's the skin of Mike Cucarisi, and Mike was like, hey, looks like a Stuart Tribert molecule. And then we were like, oh no, you can tell just by walking by? You like who made this? That's horrifying. And if the models can identify the chemist by looking at the molecule alone, then maybe the models can infer chemist intent, which means maybe they don't learn anything at all.

Brandon Anderson (0:26)

Well, they did learn something.

Ian Quigley (0:29)

They don't learn physics. We downloaded all chemicals from all chemists at Kimbell, which is the biggest repository of these things and their activities. Every chemist who published more than, I think it was 30 papers and 600 molecules. So you get all the molecules and all the stuff. It's 1,815 chemists. And then we trained a classifier to predict a chemist from looking at the molecule alone. And we got a top five accuracy of 60 percent. We chose to represent the molecules not with any chemical structures or formulas at all, but just a vector of the probability of creator. So now it's this 1,800 element, you know, vector, and it's got some probability of who made it. And you can use that representation and then train a model to predict activity from chemble. And it does as well as if you use chemical formulas. We really think that it's learning a lot of sociology. It's not learning ground truth. When you train models on the exhaust, you might be in trouble.

RJ Honaker (1:22)

Welcome to Latent Space AI for Science Podcast. I'm RJ. Hanaki, CTO of MiraOMICS. And I'm joined by my co-host, Brandon Anderson. He develops RNA therapeutics at Atomic AI.

Brandon Anderson (1:34)

Super excited to have Ian Quigley in the studio with us today. This episode is going to be a treat. Very excited. Ian is the co-founder and CEO of Leash Bio, which in my personal opinion is one of my favorite young tech bios in the last few years. At a high level, Leash is combining epistemic rigor, data collection at a crazy scale, and has a strong commitment to open science, and has an incredible series of blog posts, a lot of which we'll cover today, which I think really tells you how to do machine learning in a very hard domain in the right way. I think I'll have some fun. I know dogs hopefully can trickle in through the episode. But yeah, to start with, thanks for being here. And you know, what's your...

Is there anything about us that you want to tell us before we get started?

Ian Quigley (2:22)

No, thanks. That was very kind. Yeah, we founded Leash historically after being confronted by a particular problem of trying to predict small molecules binding to protein targets and then finding that the data that was out there was kind of wanting. And so we went out and built this thing at a reasonable scale to kind of try to go after it. Yeah.

Brandon Anderson (2:40)

So, reasonable scale. You're founded upon the idea of doing something called a DNA encoded library. Every person I've ever met who has touched a DELL has come out severely traumatized.

I don't know any person who has ever said DELLs, that's a great idea. Let's do hundreds or thousands of these things. So, why? What? And also, please explain what a DELL is before you get started.

Ian Quigley (3:02)

Yes.

So, when you're going after a small molecule of drug discovery, the ultimate goal is to get a small molecule or a dinky chemical to bind to the protein that you think is causing the problem in a disease, and then go from there. And some of these proteins are pretty easy to find chemical material that sticks to you and some are harder. One way to go about this is to have a lot of different chemicals in different tubes and you then dunk the protein in one tube at a time, and it kind of takes forever.

And then a different solution that was proposed some years ago now was maybe if you had a lot of chemicals in the tube at once and then one tube and then you've got a small DNA barcode that's physically attached to each small molecule, you dunk your protein in the tube kind of like a fishhook, you pull out different ones that bind, and then you sequence the DNA of those. And so the idea is that you can then pull this large number of molecules and then you wouldn't have the logistical overhead of trying to go across large numbers of tubes or individual wells and plates. And so what's happened downstream of that is that you've got chemical libraries that if you do one tube at a time, the biggest ones in the world are in the low single-digit millions. With DELs, the largest one that has been produced is 40 trillion molecules. There's some problems when they get that big, but that is the kind of scale you could try to attempt with this technology.