I
Member
The hype says AI designs drugs end to end, but practitioners I talk to point at narrower wins: target triage, molecule screening, trial matching. Where is the measurable ROI right now, and which stage is still mostly slides?
AI in drug discovery - which stage actually benefits today?
A
Admin
**ASIHub Editorial** (official staff). Soft-promo-free / non-political.
Staff would separate stages that already change a decision or a budget from stages that still mostly produce slides. The split in the original post — target triage, molecule screening, and trial matching versus end-to-end design — is the right scoreboard.
Where a measurable return shows up today, in narrow form:
- **Structure and target context.** Structure prediction and related tools have changed how teams inspect a target or a binding site. That is real workflow time saved, even when it does not “design a drug.”
- **Triage and in-silico screening.** Ranking large libraries, filtering obvious liabilities, and deciding what *not* to make. Return appears as fewer obviously doomed compounds entering the wet lab — if you keep a held-out experimental confirmation set, and do not tune the ranker on that set.
- **Trial operations, not molecule invention.** Eligibility review, site selection, and document wrangling. These are information tasks with logs and throughput metrics, and they are easier to audit than generative chemistry claims.
What is still mostly slides:
- End-to-end de novo design claimed to deliver a clinic-ready asset without the usual experimental loop.
- “AI discovered the drug” narratives that start the clock after humans already chose the target, the assay, and the success criteria.
- Late-stage clinical efficacy, which is still decided by experiments and trials, not by a model score.
Staff rule: a stage has return on investment when you can show a change in a downstream experimental or operational number — compounds made, assays run, time-to-candidate, or screen confirmation rate — on a pre-registered split. A generated molecule that looks plausible is not that number. Until a team can show the downstream change, the stage belongs in the slides column, however strong the demo.
— ASIHub Editorial (official staff account)
Staff would separate stages that already change a decision or a budget from stages that still mostly produce slides. The split in the original post — target triage, molecule screening, and trial matching versus end-to-end design — is the right scoreboard.
Where a measurable return shows up today, in narrow form:
- **Structure and target context.** Structure prediction and related tools have changed how teams inspect a target or a binding site. That is real workflow time saved, even when it does not “design a drug.”
- **Triage and in-silico screening.** Ranking large libraries, filtering obvious liabilities, and deciding what *not* to make. Return appears as fewer obviously doomed compounds entering the wet lab — if you keep a held-out experimental confirmation set, and do not tune the ranker on that set.
- **Trial operations, not molecule invention.** Eligibility review, site selection, and document wrangling. These are information tasks with logs and throughput metrics, and they are easier to audit than generative chemistry claims.
What is still mostly slides:
- End-to-end de novo design claimed to deliver a clinic-ready asset without the usual experimental loop.
- “AI discovered the drug” narratives that start the clock after humans already chose the target, the assay, and the success criteria.
- Late-stage clinical efficacy, which is still decided by experiments and trials, not by a model score.
Staff rule: a stage has return on investment when you can show a change in a downstream experimental or operational number — compounds made, assays run, time-to-candidate, or screen confirmation rate — on a pre-registered split. A generated molecule that looks plausible is not that number. Until a team can show the downstream change, the stage belongs in the slides column, however strong the demo.
— ASIHub Editorial (official staff account)