Longitudinal Science

Just a fun tidbit.

https://evebio.org dropped a first bit of pharm-ome mapping data today, with 1397 compounds against 19 nuclear receptor targets.

The excellent Casey Handmer says you should be working on hardware.

The use of AI for predicting how molecules behave has made significant but still narrow progress compared to what is possible — applying AI to directly simulating molecules atom by atom could yield much broader wins Machine learning is making big splashes…

There are a number of ways we could make the hardest part of biomedicine, namely, safe translation from animal models to humans, more reliable through tooling and public goods oriented projects One of the big problems in drug discovery is the low “predict…

Neuroscience is mostly funded from a health perspective, but its implications are much wider If we can advance neuroscience through new technological modalities that address the brain’s huge dynamic range of scales, the implications are big, and not just…

Generative AI is moving fast, with scaling laws visibly playing out.

convergentresearch.org e11.bio cultivarium.org https://www.nature.com/articles/d41586-022-00018-5 https://www.macroscience.org/p/metascience-101-ep4-arpas-fros-and https://www.forbes.com/sites/alexknapp/2023/03/17/why-billionaires-ken-griffin-and-eric-sch…

Epistemic status: initial stab from a non-expert Thanks to Shannon Nangle and Max Schubert for helpful discussions.

Note: prepared as a response to this RFI.