Public chemistry data can encode the chemist, not the chemistry

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

1. 46:29

   which I found absolutely hilarious from my own personal experience, I have seen machine learning models pick up on so many weird detractors. And your blog post about molecule ownership or authorship was a really fun story. The Clever Hans post, yeah. So many years ago, 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 us, this guy named Mike Cucarisi.

2. 46:59

   And Mike was like, hey, looks like a Stuart Shriver molecule. And then we were like, oh no, you can tell just by walking by? You're like, who made this? That's horrifying. And then that haunted Andrew in particular for like many years. Like it was like six or seven years. And we joked, you know, I don't know, every couple of months we're out for beers. Like, man, we really should figure out like how bad this problem is.

3. 47:20

   Why is that a problem? Okay, so it's a problem where it's very easy to imagine you're going to try to train models to understand chemistry. So you're going to get the data from lots of groups because it's very tempting to get data from lots of groups. 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.

4. 47:44

   Well, they didn't learn something. They learned who the chemist was. Yeah, they don't learn physics, right? They're not learning the thing you're trying to get them to learn. And so we were really afraid of this. They learned that certain chemists are better than other ones. Yeah, we learned all kinds of stuff, but maybe to set this up for the audience, so we went out and like, We published this over Christmas.

5. 48:00

   You could look it up. We have a leaderboard. We have a goofy online game you can play. But we downloaded all chemicals from all chemists at Kemble, 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.

6. 48:25

   And we got a top five accuracy of 60%. it's a very, very easy problem. Yeah. Yeah, it's super easy for the models to do. Where they get confused is where you have two chemists who run the same publications and were at the same institution. And so what we think is very likely is that there is a distinctive chemical signature style that individuals have when they make molecules. And then when those individuals have students or trainees, then they adopt some fraction of that, you know.

7. 48:53

   And so that seemed pretty clear to us. And so that was horrifying enough on its own And this is a data set that many, many academic projects, many PhDs have trained on, tried to predict on, find patterns. And this is really like a major data set for the field. Yeah, like if you are not generating your own data, you're using this. And then if you are generating your own data, then you are probably victim to this because your organization probably has multiple chemists who have their own distinctive chemical styles and that they are using their styles to go after different types of projects.

8. 49:28

   But the key here, I think, is that those chemists also tend to solve similar problems. Like I have a career here and I have a style, but I also am working on a class of problems. Right. And so that your style is also, you're learning the style instead of the problem. You're learning the style, you're learning the sociology of the problem. And the other thing that you're learning is that when you're a chemist at these organizations, you know, ordering a molecule to be manufactured and tested is kind of a pain, right?

9. 49:59

   So what will happen is you will make many, many choices of things you will not pursue and you do not write those down. Oh, yeah, it would be dumb to do that. And it's like, yeah, that's their motivation. Like, I was like, our team couldn't just, just wouldn't make it. Like, I could say, like, we could do this, but then they would cost too much. Or the lab team will say, the chemistry team will say, no, we can't make it now.

10. 50:21

    Or, you know, like, we don't have the building blocks on. I don't think it's going to work. There's all these reasons why you might not make a choice. And then not making that choice is a fingerprint that is going to land on what does get made. And then when you talk to some medicinal chemists, some of them are, bless them, they're superstitious people. And some of them will say like, oh. Yeah, I bring this up like every single time.

11. 50:45

    Medicinal chemists are some of the most superstitious people I've ever met. Right. And so it's like, oh, well, this building block worked in this program that made it to the clinic. I'm really fond of it. Maybe it helps with side building. So I'm going to put it in things that I like. And so then your model will learn like, oh, the chemist likes this because they put, so that means it's probably working.

12. 51:04

    And so it learns all this weird stuff. And so like, we were very afraid of this. And so we took, you know, we ran this model to identify who the chemist was. And then 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 1800 element, you know, vector, and it's got some probability of who made it.

13. 51:27

    And you can use that representation and then train a model to predict activity from Kimball. And it does as well as if you use chemical formulas. And if you combine them— You're coding the sociology only. Yes. If you combine them, it doesn't mean meaningfully better. And so, yeah, we really think that it's learning a lot of sociology. It's not learning ground truth. It's just learning, like, the choices and the fingerprints of the people who are trying to develop these molecules.

14. 51:54

    And it's not a knock on them, right? I mean, that's not what they're trying to do. They're trying to, you know, get to drugs by making the fewest number of chemical series possible. Of course, they're going to do it like that. But when you train models on the exhaust, you might be in trouble. So this means that this is not a great machine learning training set. We do not believe so.