But if that 20% is not the bottleneck then at most your market is replacing the labor required to get that 20% to the next phase, plus you will probably not be able to capture full value here because of lab/model competition.
Choosing the molecules is by far the most important part of drug discovery. It may only be 20% of the work, but everything downstream relies on it. If ai is able to change the odds of discovering a molecule that ends up passing phase 3 from eg 5% to 10% then you only have to do half as many trials to get the same number of drugs. So even though drug discovery doesn’t take a ton of time doing a better job at it can save huge amounts of time down the line.
Already 20 years ago, medical corporations had facilities to scan millions of molecules as potential drugs candidates. This did use knowledge found manually over the years and vast computing resources. This already found more good candidates than what could be clinically tested. To find drug candidates is not remotely the bottleneck in drug development, so unless the candidates are vastly higher quality, nothing is gained.
It’s only a benefit if it doesn’t bog the next stage down in noise. At that point it could be detrimental.
We already see that with software. It will be exactly the same with drug design.
My daughter is a leading biochem at one of the big pharma companies and that’s their principal worry about this. It’s really easy to create a lot of information these days which compromises the entire process.
The next step is already mostly noise. Less than 15% of drugs that go through phase 1 trials end up passing phase 3. The goal here isn’t to do drug discovery faster, it’s to get that 15% number up.
It's also not the magical thing that will solve all diseases like some people who are starting to think we're going to enter the singularity soon seem to think.
