Thank you for the questions!
AI has made huge inroads into tough problems like protein folding. Huge credit to Deepmind and so many others there!
We’ve gone after a different problem than AlphaFold (and others). Can we understand the function of all the proteins in our body without necessarily needing to know the structure? If one could understand cause and effect of all the proteins (when they are overactive, not present, or broken, etc), we could start to better understand what protein to target… and that is important because 90% of drugs that go into clinical trials fail and most often that is because the wrong target is picked.
In terms of successes predicting the results of experiments — we can test ourselves by looking for “ground truths” about biology and chemistry – relationships and pathways that have been proven out in humans – that show up in our maps of biology and chemistry. When our teams search the map and see landmarks they expect, it gives them (and us) extra confidence to explore new ideas surfaced there.
And to your final question – while I can’t say exactly what we’ll charge for future medicines because we’re still fairly early in the development process, I do believe the best way to bring down drug prices is to industrialize the drug discovery process. If we can find a way to scale our pipeline, bringing better medicines to patients faster, with less failure, we can start to bend the cost curve. That’s our goal in the coming decades.