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15 results for “undruggable targets”
“AI models have no defense against hidden crypto wallet stealing commands”
certain tokens as trusted and and certain, tokens as untrusted. And that means, when those tokens end up in the model, you could have someone that puts, like, a blog post online. It looks like a, you
31:32 / 32:06
market insight31:32 – 32:06
“Why the first AI-generated drug won't exist for another decade”
...them undruggable targets we've not been able to drug ourselves, we think are are interesting. They're working on drugging them using AlphaFold three,
33:18 / 33:47
prediction33:18 – 33:47
...them undruggable targets we've not been able to drug ourselves, we think are are interesting. They're working on drugging them using AlphaFold three, where Bolt's also being a similar kind of technology that could be used for that. You have others th
32:51 / 34:00
from transcript32:51 – 34:00
Sometimes they become apoptotic. Sometimes they become cell cycle rested. They become stressed. They're metabolically starved. They're hungry in some way. And so if you have this, sort of this representation of universal sort of cell space, right, ca
11:51 / 13:04
from transcript11:51 – 13:04
...targets. And so that sort of, like, gave to the paper a lot of, you know, validation to the model, a lot of validation that was kind of, wide. And so those would be therapeutics for those animals, or are they relevant to humans as well? They're relev
56:59 / 58:08
from transcript56:59 – 58:08
So this tackles the delivery problem at two different levels. One, what's the best tissue to secrete proteins and the liver produces 90 some percent of the proteins in your blood. And then what's a good way distributing it to the blood is one of the
9:45 / 10:48
from transcript9:45 – 10:48
One of the lines in the book that really stuck with me is the cure is where the disease is not, and the disease is where the cure is not. Henry was at a hospital. So it would seem that the cure is there. What's the challenge there? What do we need to
7:25 / 8:32
from transcript7:25 – 8:32
There's 20 different amino acids. So if you were to change just one of those amino acids and perturb them a little bit, you have 20 to the three hundred and sixtieth power. That's how many changes you could make to just that o protein to just to try
1:14:58 / 1:16:02
from transcript1:14:58 – 1:16:02
And and drug resistant tuberculosis is rising, not falling. Right? And so is the threat of the spread of drug resistant tuberculosis enough to generate the market incentives to to spend more to develop new cures or new approaches to to tuberculosis,
14:16 / 15:21
from transcript14:16 – 15:21
...targets like two fifty three that so many companies try to target and no one really managed to get to molecules that really works or, like, it worked, but the target product profile wasn't desirable. Yeah. I think until we clear out that list, there'
30:36 / 31:48
from transcript30:36 – 31:48
difficulty hierarchy we should have in our minds, or is it just, like, unexplored space in terms of enzymes and peptides, small molecules, other domains? Yeah. It's actually a lot more than just success rates. There's lots of properties that need to
27:01 / 28:10
from transcript27:01 – 28:10
...nine targets from the PDB filtering to things where there is no known interaction, in, in the PDB. So basically the model has never seen kind of this particular protein bound or similar protein bound to another protein. So there is no way that the mo
1:00:44 / 1:01:46
from transcript1:00:44 – 1:01:46
target for intervention actually relevant to the disease? That's basic biology, and I think we're making great strides there in understanding biological processes. Some of it is big data. Some of it is just old school grunt work, but there's a lot of
15:45 / 16:50
from transcript15:45 – 16:50
...The targets were pretty consensus. And I think biology is, like, one of the areas where you really don't want to be contrarian. You don't want to be the only company pursuing some obscure mechanism. Usually, you want to have some literature validatio
53:58 / 55:00
from transcript53:58 – 55:00
...targets, in a tissue or cell type specific way. Right? And so, you know, I think, you know, part of why biology is slow is because there's just this Russian nesting doll of complexity,
23:54 / 25:01
from transcript23:54 – 25:01
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