Speaker unverified: belief

Transcript context

…e way that we target certain proteins. Interfere, interact with, you know, maybe pathways that are existing in the cell. One question that has come up is you talk a lot about user interface and so on. And I think this is really important, but like my experience with dealing with medicinal chemists, when you get the machine learning models, is they are the most superstitious, skeptical, like pseudo-religious people I’ve ever talked to when it comes to doing science. Sorry for the medicinal chemists listening. Yeah, they’re amazing. Like, they’re absolutely, I’ve worked with some spectacular medicinal chemists who just pull magic out of their hat again and again, and I have no idea how they do it. But when you bring them a machine learning model, it is sometimes quite tricky to get them to deal with it. How has your interaction been with this? And how have you thought about, like, building Bolt’s lab to work with the skeptics? One of the great value unlocks for us and for our product has been when we brought to the team a medicinal chemist. His name is Jeffrey. So I think kind of like on the one hand, you know, day one, you know, he obviously had a lot of opinions on kind of a lot of the ways that we should change, you know, both kind of the way that the agents worked, the way that the platform worked. But it’s been really amazing kind of, you know, once also we started kind of shaping kind of the platform in a better way with this feedback, how we went from, you know, to some extent, you know, a fair skepticism to him, you know, actually using, you know, a lot of the things that we did. Yeah. So he’s doing a lot more compute than any of our computational folks in the team, you know, at times that, you know, he’s, you know, running, you know, he has all these sort of hypotheses. Okay, maybe I can hit this protein this particular way. I can hit in that way. Actually, let me look at for this particular molecular space. Let me try to optimize for this particular interactions. So he ends up, you know, running several screens in parallel, you know, using hundreds of GPUs, you know, on his own. And, you know, so this has been, you know, pretty incredible to see kind of how, you know, maybe the way that I was more thinking about a problem, which is, okay, you’re just trying to design a binder, a small molecule to a particular protein. The way that he thinks about it is, you know, much more deeply and, you know, trying all these different things, these different hypotheses. And then, you know, once he gets the results from the model, he doesn’t just, you know, take the top 15, but he really kind of looks over and, you know, kind of tries to understand, you know, the different things. And then when we select, you know, maybe some designs to bring forth, you know, he has, you know, something where, you know, both the models understand that something’s good, but himself as well. And that’s why we also built kind of the platform to be an interface for, you know, this kind of chemist and, you know, also like engineers. Yeah. Collaborative experience. good, but himself as well. And that’s why we also built kind of the platform to be an interface for, you know, this kind of chemist and, you know, also like engineers. Yeah. Collaborative experience. I think at the end of the day, like, you know, for people to be convinced, you have to show them something that they didn’t think was possible. And until you have that aha moment, you know, I think the skepticism will remain. But then when, you know, every once in a while, I think there’s like a result that like really surprises people. And then it’s like, oh, wow, okay, this is actually, I can do something with this. So you just get in their hands, have them try it out, and they’ll be convinced. Yeah, or like maybe once the lab results come back. Or their friends. Yeah, or maybe one of their colleagues is convinced. Yeah. I think it takes going to the lab at some point. There’s no avoiding that, you know, as beautiful as the platform can be, as nice as the molecules might look, you know, that the model predicted. I think what really convinces people is like, you know, hits. Yeah. Yeah. You see the results. Exactly. Yeah. Cool. Thank you for, you know, taking the time to chat with us. Yeah. You know, is there anything that you would like your audience to know? I mean, first of all, you know, we’re just getting started, you know, continuing to build a team. And so definitely always looking for great folks, both on the kind of, you know, software side, you know, machine learning side, but also scientists to join the team and help us, you know, shape. On the infrastructure side, too. Indeed. If you think that if you want a new challenge, because this is not just next token prediction, this is really a new engineering challenge. Exactly. Yeah. If you, if no matter, you know, how much experience you have with, you know, biologists and chemistry, if you want to come, you know, help us in a shape, what, you know, biology and chemistry, hopefully we’ll look like in five, 10 years. We’d love to hear from you. And so go to boltz.bio and, you know, come join the team. Cool. Thank you. Awesome. Thank you so much. Thank you.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.