ray__

It's true that clinical trials are a big cost center for drug development, but there is an argument for mitigating failure here earlier in the process through better technology development. Some of this will come from AI and some of it from better experimental methods for probing biology. One of my favorite efforts in this area is OpenADMET, which combines ML and experimental methods for addressing tox-related issues in drug discovery, which are common causes for failure in the later (more expensive) clinical trial stages:

https://www.nature.com/articles/s41467-026-73410-8 https://openadmet.org

Presumably the underlying model here is also an LLM? To what degree is it "fine-tuned", or is it just given a set of tools to build a good picture of cluster usage?

To be clear I don't think that LLMs are sentient, but the appeal in studying them is similar to biology in that you get to dissect a highly complex system with comparatively crude tools.

The end of this short interview with Stuart Schreiber has a similar vibe:

https://www.nature.com/articles/s41570-026-00803-0

Note how interested he is in consciousness and AGI. This is something that he's been talking about for a long time, just formulated differently. You need to be able to temper true scientific rigor with a little bit of wackiness to even think about tackling these big questions.

I don't however think that this will solve spaces on MacOS, for the simple reason that opening new instances of apps is inconsistent and often doesn't behave how you'd expect it to once one more than one space is involved (in my experience, anecdotal).

I've come to peace with the fact that I will never be able to simultaneously experience the productivity of i3 and the necessary evil of MS Office/Illustrator on the same OS. The most important factor in my work is who I work with (rather than what I work with) so I'll remain on the latter train for now.

ARPA-H was a great step towards this goal for public health-focused efforts (-omics experiments aren't going to pay for themselves, at least at first) but a more general funding mechanism has been needed. I think this is a great direction for the NSF, and to be honest it's refreshing to see something like this given the horrible stance that this government has taken towards science (which has been compounded by the biotech bubble/correction).

Unrelated, but I don't know why I expected the website and editor theme to be hay-yellow and or hay-yellow and black instead of the classic purple on black :)

1) Sadly there isn't really. There are a few good blogs like Derek Lowe's "In the Pipeline" that centralize news, but no anonymous online forum like this.

2) Google scholar alerts, Twitter, Bluesky, and word of mouth.

3) I think our understanding of biological processes at the mesoscale is about to hit an inflection point, largely through advances in electron microscopy (cryo-ET) and the ability to perform simulations at this scale.

4) Not harder but definitely more messy and progress is less linear.

It reminds me of the poem composed from one of Trump's tweets: "O, the Pelican. so smoothly doth he crest. a wind god!" There are lots of other examples on r/othepelican.

https://www.aliexpress.us/item/3256804264671858.html

I didn't bother to mess around with mounting, I just used command strips to attach the driver board to the back of the monitor and mounted the monitor on one of these clamp-type stands:

https://www.amazon.com/dp/B01MFDQR5D

The heat sink on the board can get warm but nothing to worry about regarding being mounted to the monitor.

With "induced proximity" approaches like the one in this study, all you need is a molecule that binds the target protein somewhere. This idea has been validated extensively in the field of "targeted protein degradation", where a target protein and an E3 ubiquitin ligase, a protein that recruits the cell's native proteolysis machinery, are recruited to each other. The target protein doesn't have to be inactivated by the therapeutic molecule because the proteolysis machinery destroys it, so requirement #3 from above is effectively removed.

The molecule in this study does something similar to targeted protein degradation, but this time using a protein that effects gene expression instead of one that recruits proteolysis machinery. The article focuses on the fact that cancers are addicted to BCL6. This is an important innovation in the study and an active area of research (another example at [1]), but leaves out the fact that these induced proximity platforms are much more generalizable than traditional small molecules because it's the proteins that they recruit that do all the work rather than the molecules themselves. This study goes a long way to validate this principle, pioneered by targeted protein degradation and PROTACs, and shows that it can be applied broadly.

[1] https://www.biorxiv.org/content/10.1101/2024.07.27.605429v1

The physical rotation/tumbling of molecules in an MRI is also very important, because the strong magnetic field is the thing inducing the "up"-vs-"down" split in the first place, and if the molecular motion is happening at a certain frequency with respect to the external magnetic field there are other interactions that can come into play which can affect the coherence of the nuclear spins (i.e. they can fall out of sync). Thankfully, the rotation of a small molecule like pyruvate is very fast (might higher then the "spin" frequency-a.k.a the Larmor frequenct of 13C at the magnetic field strengths involved in MRI) so the physical tumbling of pyruvate doesn't really come into play when trying to measure its signal. It can be another story for molecules that don't tumble quickly, like the ones that make up tissues, fat, etc.