Lee Cronin

I would be very interested in how we can get cross-domain training cheaply in chemical systems, because I'm a chemist, and the only thing I know of is the human brain, but maybe that's just me being boring and predictable and not novel.

Yeah. I mean, this is one that goes with my team. I try and do things that are obvious but non-obvious in certain areas. And one of the things I was always asking about in chemistry, people like to represent molecules as graphs. And it's quite difficult. It's really hard. If you're doing AI in chemistry, you really want to basically have good representations. You can generate new molecules.

Yeah. I mean, this is one that goes with my team. I try and do things that are obvious but non-obvious in certain areas. And one of the things I was always asking about in chemistry, people like to represent molecules as graphs. And it's quite difficult. It's really hard. If you're doing AI in chemistry, you really want to basically have good representations. You can generate new molecules.

Yeah. I mean, this is one that goes with my team. I try and do things that are obvious but non-obvious in certain areas. And one of the things I was always asking about in chemistry, people like to represent molecules as graphs. And it's quite difficult. It's really hard. If you're doing AI in chemistry, you really want to basically have good representations. You can generate new molecules.

They're interesting. And I was thinking, well, molecules aren't really graphs. And they're not continuously differentiable.

They're interesting. And I was thinking, well, molecules aren't really graphs. And they're not continuously differentiable.

They're interesting. And I was thinking, well, molecules aren't really graphs. And they're not continuously differentiable.

could i do something that was continuously differentiable i was like well molecules are actually made up of electron density so they got thinking say well okay could there be a way where we could just basically take a um take a database of readily solved electron densities for millions of molecules so we took the electron density for millions of molecules and just train the model to put to learn what electron density is

could i do something that was continuously differentiable i was like well molecules are actually made up of electron density so they got thinking say well okay could there be a way where we could just basically take a um take a database of readily solved electron densities for millions of molecules so we took the electron density for millions of molecules and just train the model to put to learn what electron density is

could i do something that was continuously differentiable i was like well molecules are actually made up of electron density so they got thinking say well okay could there be a way where we could just basically take a um take a database of readily solved electron densities for millions of molecules so we took the electron density for millions of molecules and just train the model to put to learn what electron density is

And so what we built was a system that you literally could give it a... Let's say you could take a protein that has a particular active site or a cup with a certain hole in it. You pour noise into it. And with a GPT, you turn the noise into electron density. And then, in this case, it hallucinates like all of them do.

And so what we built was a system that you literally could give it a... Let's say you could take a protein that has a particular active site or a cup with a certain hole in it. You pour noise into it. And with a GPT, you turn the noise into electron density. And then, in this case, it hallucinates like all of them do.

And so what we built was a system that you literally could give it a... Let's say you could take a protein that has a particular active site or a cup with a certain hole in it. You pour noise into it. And with a GPT, you turn the noise into electron density. And then, in this case, it hallucinates like all of them do.

But the hallucinations are good because it means I don't have to train on such a large, such a huge data set. Because these data sets are very expensive. Because how do you produce it? So... So go back a step. So you've got all these molecules in this data set, but what you've literally done is a quantum mechanical calculation where you produce electron densities for each molecule.

But the hallucinations are good because it means I don't have to train on such a large, such a huge data set. Because these data sets are very expensive. Because how do you produce it? So... So go back a step. So you've got all these molecules in this data set, but what you've literally done is a quantum mechanical calculation where you produce electron densities for each molecule.

But the hallucinations are good because it means I don't have to train on such a large, such a huge data set. Because these data sets are very expensive. Because how do you produce it? So... So go back a step. So you've got all these molecules in this data set, but what you've literally done is a quantum mechanical calculation where you produce electron densities for each molecule.

So you say, oh, this representation of this molecule has these electron densities associated with it. So you know what the representation is and you train the neural network to know what electron density is. So then you give it an unknown pocket. You pour in noise and you say, right, produce me electron density. It produces electron density that doesn't look ridiculous.

So you say, oh, this representation of this molecule has these electron densities associated with it. So you know what the representation is and you train the neural network to know what electron density is. So then you give it an unknown pocket. You pour in noise and you say, right, produce me electron density. It produces electron density that doesn't look ridiculous.

So you say, oh, this representation of this molecule has these electron densities associated with it. So you know what the representation is and you train the neural network to know what electron density is. So then you give it an unknown pocket. You pour in noise and you say, right, produce me electron density. It produces electron density that doesn't look ridiculous.

And what we did in this case is we produced electron density that maximizes the electrostatic potential, so the stickiness, but minimizes what we call the steric hindrance, so the overlaps that's repulsive. So, you know, make the perfect fit. And then we then used a kind of like a chat GPT type thing to turn that electron density into what's called a smile.