I'm certain you could build an embedding that provided a utility function for molecules based on price and synthesizability. That's an approximation of what th

I'm certain you could build an embedding that provided a utility function for molecules based on price and synthesizability. That's an approximation of what the chemist's brain is doing. You wouldn't ask a chemist to evaluate the molecules (in drug discovery), though- you'd have a molecular biologist (really a lab tech) set up a screening campaign, and in many cases, the biological readout that predicts something could work as a painkiller or oncology drug is relatively straightforward to implement experimentally at scale (high throughput screening). Unfortunately those readouts aren't super-predictive of the full biology, however. I expect DeepMind or Isomorphic to announce, in the next five years, that they have made a model that can quickly identify whether a specific molecule would be likely to pass clinical trials and the rest of the FDA process. With a false negative rate ("predict that a drug would not get through to approval, but in reality it would have") below around 25%, we could easily save billions a year in failed drug costs.