I used to work at a drug discovery startup. A simple model generating directly from latent space 'discovered' some novel interactions that none of our medicinal chemists noticed e.g. it started biasing for a distribution of molecules that was totally unexpected for us.
Our chemists were split: some argued it was an artifact, others dug deep and provided some reasoning as to why the generations were sound. Keep in mind, that was a non-reasoning, very early stage model with simple feedback mechanisms for structure and molecular properties.
In the wet lab, the model turned out to be right. That was five years ago. My point is, the same moment that arrived for our chemists will be arriving soon for theoreticians.
This is really cool. Have you (or your colleagues) written anything about what you learned about ML for drug discovery?
The "25% higher mortality" comes from the all-cause mortality result in "Findings", which is one of the main results of the paper. The paper expresses it as a hazard ratio (HR) of 1.25 [1.10–1.42]. Hazard ratios are standard in medical research, but I wanted to summarize the main result without jargon.