My main point of skepticism about repurposing is whether this is giving any of new and actionable information. It seems to be reliant on pre existing target annotations, and qualified targets already have molecules designed for them. Is the off-target effect strong enough to give you a superior molecule? Why not just start by picking a qualified target and committing to designing a better molecule without doing all the off target assay stuff first?
I completely agree, but I also think there is some truth to the related statement: 'cancer research often isn't conducted in a way that is actually useful'!
For example, in-vivo tumor experiments in mice can yield completely different results depending on exactly where the tumor was implanted. E.g. a 'lung cancer mouse model' may have the lung cancer injected just under the skin, also known as subcutaneous tumor models, instead of in the lung! Entirely because it's a lot more efficient + yields more trustable data, but the results are often deeply disconnected from how the tumor would naturally grow + respond to drugs within its host organ.
Thanks for your posts! I've been very impressed with your ability to both be at the leading edge of knowledge and communicate the parts that are most interesting for a broad technical audience, it's an impressive skill.
I think it has very limited therapeutic applications with what we know about RNA structure today! But there's a great deal of completely unknown RNA biology (some of which I touch on in the essay) that may greatly benefit from RNA structure. The bit I mention about Arrakis Therapeutics preclinical work in drugging the (structured) RNA version of the MYC protein points to that being a very real possibility. All interesting biotech startups are built on bets on where the future is going, and I'm very happy that someone (AtomicAI and others) is betting on this, because clearly the answer of 'is RNA structure useful' isn't super open-and-shut
Depends on how deep you want to go. The pioneering work in this field is the "The multiscale coarse-graining method." series of papers kicked off by Noid. Who is still a person to pay attention to in this field.
I also believe the work of Frank Noe is someone to watch for in the ML potential space for proteins.
Writer of the article here: randomization fixes most of this, but the other commenters are correct in that doesnt fully account for the clinic performance (e.g. nurse performance, which does dip during the night according to the literature). I previously thought it wasn't a major issue for clinical trials, since a separate team independent from the main ward are giving the drugs, but there isn't super strong evidence to support that. I will update the article to admit this!
This said, I am inclined to believe that this isn't a major concern for chronotherapy studies, since I haven't yet seen it being raised in any paper yet as a concern and the results seem far too strong to blame entirely on 'night nurses make more mistakes'. Fully possible that that is the case! I just am on the other side of it
sure! i cover this in the essay, the purpose of this dataset is not just toxicity, but repurposing also
>toxicity of major metabolites
this is planned (and also explicitly mentioned in the article)
>no need to worry about CYP’s
again, this is about more than just toxicity
>volume of distribution
i suppose, but this feels like a strange point to raise. this dataset doesnt account for a lot of things, no biological dataset does
>advertisement
to some degree: it is! but it is also one that is free for academic usage and the only one of its kind accessible to smaller biopharmas
reply