Brandon (Host)
speaker
390 appearances
3 recordings
1 series
first heard Jun 2026
last heard 16 Jul
Brandon (Host)’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 3 in all, peaking in Jul 2026 with 2.
Appearances
What's harder, materials or biology?
And then it has all the difficulties of reasoning about biology and adverse effects and... Yeah, but the counterpoint being that we have so many tricks in our toolkit which you can borrow from biology, right?
So it's harder, but you also have... Well, I think materials are harder.
I mean, like in terms of supply chains still matter for both.
You know, maybe you replace clinical trials with some, you know, product validation and verification, I guess, qualification is the term.
So like there are direct analogies and there are hard parts for both of them.
Cool.
Well, um, yeah, before we end, is there anything you want to leave the audience with?
Um,
Let me say like why we're here.
Yeah, that's a really nice lead into my first question, which is, we've both been in this domain of machine learning for molecules and bio for roughly 10 years.
entire generation of tech bio has come and gone since then.
Well, a lot of machine learning for molecules has been, I think, quite effective.
One domain where it's been not effective has historically really resisted
machine learning modeling has been the world of protein, small molecule interactions.
And with some of recent advances that Genesis has put out, it seems like you might have actually started to make real improvement on this in a way that we haven't seen for a long time.
Can you talk about what you have done, what Genesis has done, the developments which led to improvement and why you think this is actually a real improvement over kind of some of the traditional machine learning strategies, which were ambiguously helpful?
Maybe we can go back in time a little bit and explain, like, what was the state of protein small molecule drug discovery, let's say, back then?
about a decade ago.
Like, what could we actually expect to do with machine learning models?
Showing 141–160 of 390 · page 8 of 20
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