🔬Beyond AlphaFold: How Boltz is Open-Sourcing the Future of Drug Discovery

**Jeremy Wohlwend** (0:00)
Actually, we only trained the big model once. That's how much compute we had. We could only train it once. And so while the model was training, we were finding bugs left and right. A lot of them that I wrote. And I remember us doing surgery in the middle, stopping the run, making the fix, relaunching. And we never actually went back to the start. We just kept training it with the bug fixes along the way.

**Brandon** (0:26)
Which was impossible to reproduce now.

**Jeremy Wohlwend** (0:29)
Yeah, that model has gone to such a curriculum that you learn some weird stuff. But yeah, it's somehow a miracle it worked out.

**SPEAKER_3** (0:38)
It's a pleasure to have with us today Gabriele Corso and Jeremy Wohlwend. They recently founded Boltz, a company trying to democratize and bring art structure prediction in biology to the masses. They were both recent PhD grads from MIT and have been working on all sorts of foundational papers in generative biology. Anyway, pleasure to have you here. Thanks for coming.

**Jeremy Wohlwend** (1:04)
Thank you.

**Brandon** (1:04)
Thank you.

**SPEAKER_3** (1:06)
I guess we're maybe what, six years post AlphaFold 2 right now, which was like kind of a big moment. Is that right?

**Jeremy Wohlwend** (1:13)
I think it was at 2021, so yeah, going on five years.

**SPEAKER_3** (1:18)
Five years, five years, yeah. Yeah, so maybe for the audience, let's go back to that moment in time and explain, what was this big moment and why was it interesting? Why was everyone so excited? And I think you two were probably quite excited. So why were you personally excited?

**Gabriele Corso** (1:33)
I would start on kind of why that was interesting, kind of from a scientific standpoint.
So, well, AlphaFold, so maybe first as a kind of introduction for the ones in the audience and not structural biologists. So the idea of structural biology is that we want to try to understand how proteins and other molecules take shape inside our cells and how they interact. And structural biology is sort of this beautiful discipline where we are somehow able to understand this miniscule structure at kind of atomic details using these incredibly complex methods like X-ray crystallography. And the dream has always been of computational biology. Can we understand kind of the structures without having to resolve this crystal, shoot X-rays and so on. And so AlphaFold was a real breakthrough in this problem of protein folding, which is trying to understand the structure of a single protein. And to me, it was exciting across kind of many dimensions. One, I was a computer scientist. I was working a lot on machine learning. And I saw kind of the impact that kind of the work similar, somewhat similar to what I was doing could have on like a long standing scientific problem. And on the second perspective from a more personal side, the seeing kind of the structures coming out of these models where you see kind of this beautiful creation of life is something that was very inspiring to me. And so that was kind of one of the things that led me to start working on structural biology and in particular with machine learning.

**SPEAKER_3** (3:26)
Were you a structural biologist before AlphaFold came out? I mean, did you, you did machine learning, but it was not in structural biology. So that actually shifted your career quite dramatically.

**Gabriele Corso** (3:36)
Yeah, very dramatically. I was working on some pretty kind of theoretical, methodological things, and I was starting to see kind of some of the challenges in kind of doing somewhat theoretical or methodological work and seeing kind of the potential impact of doing excellent. AlphaFold was really a machine learning breakthrough, but, you know, and applied machine learning. And so that led me to want to start working in applied ML.

**Jeremy Wohlwend** (4:06)
Our group at the time was working along like small molecules already. And I think AlphaFold is kind of what triggered, I think, this shift to like working on biologics. And at the time, I think it like opened as many questions, you know, as it answered in a sense, like we, the immediate follow-ups were, okay, like, can we do this on other things than proteins? Can we do, you know, interactions of small molecules with proteins, nuclear gasses with proteins? Can we model more complex protein systems? And I think, yeah, very rapidly, I think, after AlphaFold, people realized, I think, that there was no machine learning could have a, could really, yeah, sort of target this problem very differently than, you know, than previous methodologies.

**SPEAKER_3** (4:48)
Going back to the AlphaFold 2 moment, like, I remember this very well. I was at NeurIPS when I guess the results of this famous competition came out. So you want to talk about CASP and like what it is and why it was so interesting and exciting.