A lot of chemists I've met aren't really don't care about AI which is pretty strange to me. Their argument is that the hardest part of making molecules isn't the design process, it's in optimizing the synthetic route, reaction kinetics, or troubleshooting solubility issues that AI might not have caught. It's only going to get better, so we'll see how long their feelings are valid...
Thanks for your comment, I can relate to that as I myself met scientists (not only chemists btw) during conferences, who just so skeptical about the whole AI thing. I mean, there is a lot of hype, but there is tremendous progress as well, so it feels a little lazy of them not to try to understand the landscape and see what works and what doesn't. Speaking about synthetic route and solubility, just out of my head, there is a ChemAIRS suite which is an AI platform for pretty much everything related to retrosynthesis, and other things chemists normally care about...
This issue reads like standing on a cliff edge and watching two forces reshape the landscape at the same time — AI accelerating beyond its own shadow, and biology cracking itself open into something programmable, modular, almost language-like.
What struck me most is not the flood of innovations but the pattern underneath: everyone is trying to compress the distance between data → mechanism → therapy.
Quantum shortcuts for molecular space, foundation models for pathology, TCR atlases at population scale — all of it is a race to collapse the search time between “we think this works” and “we know why it works.”
And then there’s the other thread you didn’t spell out but is woven through the entire issue:
regulators are finally admitting they can’t keep up unless they switch paradigms too.
The FDA’s “plausible mechanism” route for individualized gene editing might be the quietest but most revolutionary line in the whole newsletter.
That’s not incremental change — that’s the beginning of medicine leaving the era of population-level treatments and entering the era of personalized interventions built on-the-fly.
What I kept thinking while reading is:
the pace isn’t just increasing — the system is starting to self-accelerate.
AI tools design molecules; multimodal datasets train the AI; regulators adapt to AI-powered biology; and new funding waves chase the possibilities opened by the first cycle.
Your roundup captures that tipping point feeling perfectly:
we’re crossing from “tech in bio” to “bio becoming an information technology.”
Curious what you think will snap first:
the scientific bottlenecks or the regulatory ones?
A lot of chemists I've met aren't really don't care about AI which is pretty strange to me. Their argument is that the hardest part of making molecules isn't the design process, it's in optimizing the synthetic route, reaction kinetics, or troubleshooting solubility issues that AI might not have caught. It's only going to get better, so we'll see how long their feelings are valid...
Thanks for your comment, I can relate to that as I myself met scientists (not only chemists btw) during conferences, who just so skeptical about the whole AI thing. I mean, there is a lot of hype, but there is tremendous progress as well, so it feels a little lazy of them not to try to understand the landscape and see what works and what doesn't. Speaking about synthetic route and solubility, just out of my head, there is a ChemAIRS suite which is an AI platform for pretty much everything related to retrosynthesis, and other things chemists normally care about...
This issue reads like standing on a cliff edge and watching two forces reshape the landscape at the same time — AI accelerating beyond its own shadow, and biology cracking itself open into something programmable, modular, almost language-like.
What struck me most is not the flood of innovations but the pattern underneath: everyone is trying to compress the distance between data → mechanism → therapy.
Quantum shortcuts for molecular space, foundation models for pathology, TCR atlases at population scale — all of it is a race to collapse the search time between “we think this works” and “we know why it works.”
And then there’s the other thread you didn’t spell out but is woven through the entire issue:
regulators are finally admitting they can’t keep up unless they switch paradigms too.
The FDA’s “plausible mechanism” route for individualized gene editing might be the quietest but most revolutionary line in the whole newsletter.
That’s not incremental change — that’s the beginning of medicine leaving the era of population-level treatments and entering the era of personalized interventions built on-the-fly.
What I kept thinking while reading is:
the pace isn’t just increasing — the system is starting to self-accelerate.
AI tools design molecules; multimodal datasets train the AI; regulators adapt to AI-powered biology; and new funding waves chase the possibilities opened by the first cycle.
Your roundup captures that tipping point feeling perfectly:
we’re crossing from “tech in bio” to “bio becoming an information technology.”
Curious what you think will snap first:
the scientific bottlenecks or the regulatory ones?