It's 10 to the negative 15 seconds. So you're moving really, really slowly. But you have to move really slowly, because the that all of the bonds are vibrating. And if you move faster than the bonds are vibrating, then the whole simulation gets messed up, just like when you're watching one of those movies where people have the flip frames, like if you skip over 10 frames, who knows what's gonna happen. So I guess if you don't skip over 10 frames, we have the person take one step, and then all of a sudden they're running, it just doesn't work. So people move at this really small scale, then you have to get at least a microsecond, which is 10 to the negative six. And so to go from 10 to the negative nine to 10 to the negative six, it's a lot of simulation steps. And so that's how people have simulated proteins moving. And the key part is one, doing broad sampling, and two, having an accurate forcefield. And that's where New Equilibrium is coming in with all of our buzzwords, because traditionally, these force fields were developed in the 70s, and actually, Martin Carr plusa couple of other people got the Nobel Prize in 2013, or 14, for the work that they've done in developing this molecular dynamic simulation architecture in the 70s. And those are functions that are based on the physics of proteins, it looks at how bonds move, and how bonds stretch and how bonds twist. So stretching, twisting, and bending. And then also non bonded interactions that have to do with electrostatics, and everything. And so this goes into this one function that you calculate in every single step. And I've gotten really detailed on the computations. But basically, you do these simulations, and then you pull out if it's an intrinsically disordered protein, all of the confirmations that are sampled a lot, and you say like, "Okay, this protein samples, like these 20 confirmations," and then you can try to show that it agrees with whatever experimental data is available. The challenge has been that while this works really well for folding proteins, the forcefield, this big function, is based on folded proteins. All of the training data that went into it came from folded proteins. And so over the years, people have found that when they simulate intrinsically disordered proteins, they're getting too much structure, everything's more compact than it should be according to experiment. And everything has more basic protein secondary structures, like Ulysses and beta sheets. And so they don't match what's in the lab, because there's this huge bias of the computations to folded proteins, and so when I first started thinking about New Equilibrium, I was reading a lot about like startups and what startups should do and how to have an idea, because I knew we needed a better way to simulate intrinsically disordered proteins. But I didn't know how. And then I read this, like blog or Medium post by Elon Musk about first principles thinking. And the idea was that you like, you take the problem. And then you break it down. And you're like, Well, what tools do I have in 2018, or 19, to solve this problem instead of iterating? On how people had solved it previously, because there has been a lot of iteration where people have said, like, okay, these proteins don't work well or these simulations didn't work well for intrinsically disordered proteins, let's change this parameter or run it this way. But it's always been kind of like gradually tweaking on what was already there. And so I was like, "Oh, well, Elon Musk knows how to build a startup. So I'll do what he does, like, what do we have in 2019? That is a tool that I should be using if we're going to be building a new way to do things." And AI was just the buzzword everywhere. And so then I started thinking, "Okay, well, how do we use AI for simulations?" And that work had been going on in the small molecule field of simulating energies for small molecules. And so I was like, "Okay, well, how do we expand that to proteins, that's going to be what New Equilibrium does." And so that's where the AI came in: to get the accurate agreement and to move away from training data on folded proteins, but to look at training data on quantum chemistry and other things that we can get now better than we could in the 70s for intrinsically disordered proteins. And so that's a big part of it. And then the quantum computing kind of thinking in the future that we're also going to really need to accelerate these, and so when we're making our decisions now about these new algorithms we're building we should be thinking about like, "well, will this actually work on a quantum computer? What what could we do differently so that it would" and so it's really... I think I said in the press release, it's building for the future, but that's what we're doing because we don't want to build something now when we know there's this great new technology coming and it's not even going to work for that. So we're keeping that in mind in everything we do.