Have a comment below on this, but generally things don't work out in the clinic because we didn't understand the biology adequately, and not because we didn't have a molecule.
A better binder only helps if you know what to bind to and that if you bind to it, you'll have the desired effect.
This is why the industry has crowded around a very limited set of "validated" targets. It's because we know if we do something with them, they will work. Obviously we're likely missing a lot of great ideas though.
AI might help find some of those ideas, but we won't know until someone tests them in a human. Essentially, without more extremely expensive and difficult data to acquire, it's not likely to be that much better at picking winners.
I'm in the sector and I believe strongly in AI helping (I've bet my entire career on it). It will get there eventually, but it will take much longer than people seem to think at the big labs.
On crowding in big pharma: https://www.lek.com/insights/life-sciences-pharma/biopharma-...
Parent comment reached out asking for examples so thought I'd post my answer here:
We thought clearing amyloid beta plaques would slow alzheimer’s disease. We now have antibodies that do clear the plaques quite effectively, however they barely show any clinical benefit. Some trials failed entirely, others cleared a statistical bar that clinicians agree would be hard to detect by someone just living with the person. Essentially all patients given this drug still decline at more or less the same rate they declined before getting the drug. Definitely not the “we took away the cause of the disease, so at least they’ll stay about the same as they are today” effect we were hoping for.
To find this out, many trials costing (hard to get the specific number) but hundreds of millions of dollars had to be run. And this was easily the consensus target for solving Alzheimer’s.
If AI magically comes up with a new idea (Eliminate Protein X and Alzheimer’s will stop in its tracks), it will take us that long and that much money to know if it was right.
And before that we’ll have to run a huge number of lab experiments, likely involving developing new model systems (engineered mice usually, but not always) to test the concept before a human gets the drug.
So even assuming an AI model today is a “Genius” level talent, and that the target we need to go after is really already in some pre-existing paper or dataset, it will take a huge amount of investment to know.
And again, likely we need more data about how Alzheimer's works, because our best ideas haven’t worked the way we expected. Maybe AI can help us design ways to get that data, or ideas for what to gather. But it’s not going to magically come up with an answer where we simply do not know.
There’s a huge cardiology trial using Lp(a) as a target that just failed because it turns out that lowering cholesterol using the drug doesn’t actually reduce cardiac events. This was the most genetically validated open target in cardiology, and it turns out to not work how we thought. Hundreds of millions of dollars lost. (https://www.cnbc.com/2026/09/08/novartis-cholesterol-setback...)
Take another unrelated example: If AI told me, “I’ve done a bunch of geology, and I know there’s a huge vat of oil under the ground over there”. You’re gonna need to drill to find out. And if you give the AI all the information you have about geology and it says, “Maybe there’s oil under the ground over there, but I can’t be sure unless you take these rock samples”. Then someone needs to go out and get the rock samples, grind them up, stick it in a machine, get the data.
All those things take time and money. Ideas are cheap, measurements cost time and money. In drug discovery, we’re often against the frontier of what we know. Past the frontier you need new data. In computational fields (coding, video games, maybe in some ways mathematics) you can convert compute into data. In biology and the physical world, you have to do measurements. Compute can’t help you at the edge of data in the physical world.