"beyond the most commonly discussed applications of AI, like proposing initial designs for new molecules". at this stage it's all waffle, all marketing, all hope. i don't know why we share these posts, it's just marketing

This was always one of the main reasons for accelerating AI. I really hope there is soon a moment for AI drug development similar to the recent AI math advances.

This is really cool, and so far also just seems to be the model of funding science and discovery that the US government and other grant making entities follow thus far. Submit for RFP to do X with Y. At ANL for example it was (and still is in Computing), use our HPC resources to accelerate your research (after of course review and approval of proposals).

Other large companies and foundations have copied this model and I would presume have given much more latitude in coloring outside the lines without red tape. Beyond that I am curious what will be innovative about this (sure, claude is amazing, but there's nothing stopping another grant making organization from saying here's a bunch of money and compute - hire great people and go wild)

Anthropic: “We’re worried there’s a 10%+ chance AI will kill all humans with a biological agent”

Also Anthropic: “we’ve given AI a biological lab to develop chemicals that impact humans!”

If they are serious on this, they will work on lab robotics and fancier and fancier organoids for automatic testing.

(No one please propose automated mice testing or the EAs will have an aneurysm, haha.)

Yo Dawg , you love hallucination so we develop drugs with LLM.

Crossing fingers for LSD 2.0

Finding and designing new drugs is a long process, but it is the least cumbersome and expensive part of the process (as opposed to human trials which run on average upwards of 4x as long and cost 10-50x as much as preclinical).

And what we need is not simply more biological phenotypical data (such as genomics, biomarkers, etc.) that we can plug into a model. We actually need that AND labeled trial outcomes, which is a challenge in and of itself because pharma companies will often hide negative trial results (which are advantageous for training the model layer) in order to exacerbate the perception of drugs that make it to market (search for "missing data phenomenon pharma"). The clinical trials are an extremely costly (and necessary) market inefficiency that leads to a bunch of bad behavior that is unfortunately self-reinforcing. I spoke on the phone with a biotech entrepreneur who told me to think of the Gavin Belson (from Silicon Valley the TV show) quote where he says "I don't know about you, but I don't want to live in a world where someone other than me makes the world a better place better than me", and to just remember that's how many people think in pharma and biotech + pharma.

Augmenting how pharma companies work is a great first step. Augmenting how candidate drugs are engineered is a good first step. But that does not change the fact that we simply do not have deterministic governing laws (as we do in physics) to understand the complete pharmacokinetics/dynamics of a drug across a large patient population. You just have to run the test. It is true that AI will help, but only as much as we have a reasonable way to predict that candidate drugs will lead to positive trial outcomes and a large market.

Note that I am not a doomer here. I think AI will have a profoundly disruptive and positive impact on the pharma industry. I'm just not sure starting with drug design alone leads to hope unless there are things happening in the background between pharma + the labs (very possible) that could lead to better trial outcomes.

Given that a lot of the bottleneck in speed is human trial, I wonder if overall speedup will be limited anyway?

Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.

These companies probably have so much data on stuff that failed that there are likely so many gems and paths we haven’t tried out yet