The one model that would actually make a huge difference in pharma velocity is one that takes a target (protein that causes disease or whatever), a drug molecule (the putative treatment for the disease), and outputs the probability the drug will be approved by the FDA, how much it will cost to get approved, and the revenue for the next ten years.

If you could run that on a few thousand targets and a few million molecules in a month, you'd be able to make a compelling argument to the committee that approves molecules to go into development (probability of approval * revenue >> cost of approval)

If you had a crystal ball that could predict the properties of the molecule, perhaps.