My sense, from working in the biotech industry, is that this is going to take much longer than people think. I have two clients using ML/AI for drug discovery and their results have been mixed. There are cases where a drug was identified by ML but then failed during animal testing.
Unfortunately, the feedback cycle in drug development is measured in years (and millions of dollars). I've seen cases where a drug works better than competitors but has a worse safety profile. The solution was to alter the drug to remove the side-effects, but that required a new trial (meaning, two more years plus ten million dollars more).
That said, I do think there is some low-hanging fruit. There are lots of datasets from past trials and it's likely that there are some patterns there that AI could mine. I don't think it will be like software engineering ("we build a Photoshop-clone in 2 hours") or math, but we might see announcements about a new drug candidate being found. Verifying it, however, will take a long time.
Unfortunately, the feedback cycle in drug development is measured in years (and millions of dollars). I've seen cases where a drug works better than competitors but has a worse safety profile. The solution was to alter the drug to remove the side-effects, but that required a new trial (meaning, two more years plus ten million dollars more).
That said, I do think there is some low-hanging fruit. There are lots of datasets from past trials and it's likely that there are some patterns there that AI could mine. I don't think it will be like software engineering ("we build a Photoshop-clone in 2 hours") or math, but we might see announcements about a new drug candidate being found. Verifying it, however, will take a long time.