Another great piece by @owl_posting. Organoids can offer a lot, but there’s a lot of complexity to make them very useful. This comes from the complexity of Alzheimer’s itself, and the challenges with organoids themselves with the nuances of molecular biology.

Great thoughts from @owl_posting on organoids as models for Alzehimers. Raises the more important general point that "all models are wrong but some are useful" and more importantly some are useful in particular ways even as they are wrong in other ways. Often in drug discovery it's unclear if a model is useful. Ideally it would discriminate between successful and unsuccseful drug but you don't know that until years later, if ever. And then even in cases where we are pretty sure a model system is useful (maybe it's predicting downstream advancement well), you often aren't sure why. With so many ways to be wrong, you can always take the negative view and point out what's almost certainly wrong, or you can try to find better things and see how they work out.

Last year, one of the best essays on endometriosis from a biotech and discovery perspective was published by Abhishaike Mahajan, known for @owl_posting. We interviewed him to learn what his most interesting findings were when researching this condition. How massively underfunded it is, considering its prognosis and DALYs (a standard measure used to calculate the total burden of disease and early death in a population), came to mind.  Read his full essay and interview here:

science advances are typically preceded by measurement advances. the closer the lab model is to the underlying phenomenon the faster we typically progress. this is a good essay laying out that logic for organoids for alzheimers in particular but i think applies to many brain diseases

i had a tweet awhile back that was like 'talking to ai models increasingly makes me think less about model intelligence and more about how smart humans are'. another banger by sholto, highly recommend a listen

imo the millennium problem list in biology should be a list of very specific datasets like ‘a catalog of epitope-resolved nm protein binders across the human proteoform in X environment’. hard because it requires knowing the full range of human ptms/isoforms and having some way to do scalable generation/testing of epitope-specific binders (computationally or otherwise). both very difficult but achievable also verifiable!

Here's more on our initial strategy behind these grants, and other grants we're investigating in Public Data for Health, that may be of interest to researchers: Our starting hypotheses for areas OpenAI Foundation support can be most useful are: connected data, scarce data, and direct data. Datasets we fund will usually have one or two of these properties, described in more detail in the next tweets, and in rare cases will share all three properties. Across all datasets, we believe making data broadly accessible, while ensuring privacy whenever that relates to patient data, will become increasingly important. This will allow researchers to build on each other’s work, and scientific progress to emerge from unexpected places.

Today we’re announcing we’ve raised $20M in seed financing led by @QuietCapital , with participation from @GradientVC , @haystackvc and @CompoundVC and other amazing folks to build the verification layer for AI-driven biology. We are also delighted to be welcoming @vqctran who is joining us from @GoogleDeepMind as a co-founder. AI is not going to cure all diseases without a verification substrate - a scalable environment where models can test predictions and learn against living human biology. AI is becoming increasingly capable of generating biological hypotheses, therapeutic candidates and experimental designs. But generating ideas is advancing much faster than our ability to test what those ideas will actually do in human biology. Existing systems force a tradeoff: the most biologically relevant approaches are slow and expensive, while faster in vitro and computational systems often lack the fidelity needed to capture human response. Polyphron’s bet is that tissue is the fastest, cheapest, most parallelizable verification substrate that still contains the biology that matters and we believe the path to biological superintelligence runs through closed-loop interaction with these living and simulated human systems. Thank you to @axios for covering the announcement. Link to our new website in the thread.

Excited to partner w/ @FoundationOAI to build (w/ OpenADMET) better HT methods to measure physicochemical properties and Distribution to enable the next set of AI model improvements through blinded competitions. Thanks @JacobTref & @owl_posting!!!

Amazing to see the @FoundationOAI funding the CTD Commons, a project that came out of @RuxandraTeslo's Launch Sequence proposal! The CTD Commons will purchase the FDA filing documents and clinical trial results from failed biotech companies to create an open-source library that will accelerate FDA approval timelines, lower compliance costs, and help unbottleneck AI's progress in the life sciences. Full project proposal:

The OpenAI Foundation (@FoundationOAI) is now hiring 20+ people (4 roles in the life-sciences team!) If you are excited about our mission to ensure AGI benefits all of humanity, I would love for you to apply or share this link with someone talented: