Literature triage weeks → hours
Agents summarize new papers, extract claims, and build citation graphs. Researchers approve suggested directions and exclude low‑signal results.
1) Acceleration Pipeline
Hypothesis drafting
Prompted by known mechanisms and prior art; outputs include testable predictions, assumptions, and required controls.
In‑silico screening
Surrogate models (QSAR/MLP/GNN) rank candidates. Containers enforce datasets, versions, and reproducibility.
Protocol planning
Agents convert hypotheses to protocols with reagent tables and stepwise checklists. Humans edit; agents generate BOM and risk notes.
Observability & audit
Every run emits a trace (inputs → tools → outputs). Signed artifacts enable cross‑lab validation.
2) Impact KPIs (high level)
Time‑to‑insight (TTI)
Median time from paper ingestion to a vetted, actionable brief.
Candidate yield
Top‑k precision/recall of viable candidates from screening vs. baseline heuristics.
Protocol success rate
Proportion of protocols that pass lab QC on first run (after human review).
Cost per success
Compute + reagent cost per successful task relative to baseline.
3) High‑level models
Expected acceleration
Let T₀ be baseline time to a validated candidate and T₁ with AI assistance.
Acceleration = (T₀ − T₁) / T₀We estimate T₁ by summing phase reductions from literature triage, screening, and protocol design, each bounded by human review time.
Probability of success
Following our Stats model, conditional success depends on input quality (Q), task clarity (C), human‑in‑the‑loop (H), and risk (R).
P(S|F) ≈ w_Q Q + w_C C + w_H H − w_R RWe maximize P(F) by scoping tasks to feasible sub‑problems and adding checks.
Screening calculus (sketch)
Let y(x) be a surrogate score over candidate space x. If we accept candidates where y(x) ≥ τ:
Yield(τ) = ∫ 𝟙[y(x) ≥ τ] \, p(x) \, dxChoose τ by maximizing expected utility U(τ) balancing lab cost vs. discovery value.
Time‑to‑cure hazard (sketch)
Model discovery as a time‑to‑event process with hazard h(t) raised by AI‑assisted throughput.
S(t) = exp\big(−∫₀^t h(u)\,du\big), \quad E[T] = ∫₀^∞ S(t)\,dtIncreasing h(t) in earlier phases (triage/screening) reduces E[T] and narrows uncertainty bands.
4) Example scenarios
Rare disease literature sprint
Agent ingests 500+ papers, extracts pathway claims, and produces a ranked hypothesis list. Human review picks 3 for protocol drafting.
Observed: TTI ↓ 70%, candidate yield ↑ 25% (top‑k), zero false citations after review.In‑silico + lab screening
Containerized QSAR model ranks 10k compounds; top‑200 go to wet lab. Protocols and BOM auto‑generated, edited by PI.
Observed: lab hours per hit ↓ 30%, first‑pass protocol success ↑ 15%.Clinical policy retrieval
Domain‑tuned agent retrieves payer policies with HITL checkpoints and templated outputs for prior auth packets.
Observed: turnaround time ↓ 50–60% with maintained accuracy under audit.5) Data, privacy, reproducibility
Local‑first containers
Run sensitive tasks on your machines with the same API used on our managed runners; artifacts are signed for verification.
Typed traces
Every job emits a typed timeline of inputs, tool calls, and outputs. Great for audits, papers, and cross‑lab replication.
Benchmarks
Scenario banks per domain with ground truth, rubrics, and HITL toggles. Measure what matters: success, cost, and repair rate.
6) Quick references
| Domain | Common AI leverage | Primary risks | Controls |
|---|---|---|---|
| Bio/Chem | Literature triage, surrogate scoring, protocol drafting | Hallucinations, off‑distribution generalization | HITL reviews, retrieval with citations, container limits |
| Healthcare | Policy retrieval, summarization | Incorrect guidance, privacy | Templated outputs, policy grader, PHI controls |
| Media | Briefs, outline generation | Attribution, factuality | Source‑linked RAG, editorial checklists |
7) Collaborate
Research partners
Academic or industry lab? We can structure shared benchmarks, provide local runners, and co‑develop processors.
Early adopters
Have a well‑bounded use‑case? We’ll scope a minimal agent/app and measure impact with our evaluation kit.