I don’t work in drug discovery, but isn’t this thought experiment skipping a lot of steps? My impression is AI is accelerating a lot of the preclinical screening and design work, but that’s pretty far from establishing “tremendously improved outcomes for patients” isn’t it? That’s still really difficult to verify, are people getting real acceleration of this part of the work with AI?
I'm working in the field for CVDs early detection of arrhythmia and ischemia with just ML. We managed to get the elusive inter-class 100% in recall/sensitivity for both which is excellent for expert cardiologist-in-the loop by using the public dataset given the the current EU/US standards of SCORE-2/PREVENT screening for CVD with very poor around 50% recall/sensitivity [1]. The significant improvement mainly in ischemia detection due to the notorious silent ischemia that's not apparent even after CT angiography or by cardiac electrophysiologist (cardiologist) inspection.
For CVDs if you can detect and confirm early it can "tremendously improved outcomes for patients" due to the current avauability of new effective drugs and treatments.
Hoping that we can get our techniques tested on real-time data since it's super difficult to find relevant physicians/doctors collaborating on the new screening techniques on anything although for our case it's non-invasive. Perhaps need to pivot for early detection of CVDs in diabetes patients instead in order to get our hands on real-time ECG data.
[1] Risk stratification for cardiovascular disease: a comparative analysis of cluster analysis and traditional prediction models:
https://academic.oup.com/eurjpc/advance-article/doi/10.1093/...