> It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost.
Maybe this kind of AI-based exploration would lower the costs. The more something is automated, the cheaper it should be to test many concepts in parallel.
A med chemist can sit down with a known drug, and generate 50 analogs in LiveDesign in an afternoon. One of those analogs may have less CYP inhibition, or better blood brain barrier penetration, or slightly higher potency or something. Or maybe they use an enumeration method and generate 50k analogs in one afternoon.
But no one is going to bring it to market because it costs millions and millions to synthesize, get through PK, ADMET, mouse, rat and dog tox, clinicals, etc. And the FDA won't approve marginal drugs, they need to be significantly better than the SoC (with some exceptions).
Point is, coming up with new ideas is cheap, easy, and doesn't need help. Synthesizing and testing is expensive and difficult.
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)
It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost.
What is challenging is culling ideas, and having enough SNR in your readouts to really trust them.