ReactionIQ runs the closed-loop optimisation engine inside MolTrace. Bayesian acquisition over a live Gaussian-process surrogate proposes the conditions most worth trying next — under hard constraints from your spectroscopy evidence and regulatory framework.
Why this exists
A typical process-chemistry campaign runs 40–80 reactions to find a robust optimum. Most of those reactions are wasted — they sample regions the team already knew were bad, or repeat conditions a previous campaign explored two years ago and forgot.
At the same time, regulatory burden is climbing. Every condition tried is a potential data point that needs to be traceable. Every impurity that crosses an ICH limit is a campaign-restart event. Spreadsheet-tracked reaction histories can't withstand inspection — and don't survive the analyst who wrote them.
ReactionIQ runs the acquisition function in your stead. It remembers every prior campaign, respects every active regulatory constraint, and proposes the experiment most likely to advance the Pareto frontier — with an auditable trail of why.
The optimisation loop
Each round emits a typed Pydantic record. The surrogate state, the acquisition proposal, the measured outcome, and the human decision are all recipe-hash-linked so any prior campaign replays bit-identically.
Set the design space: input variables (solvent, temperature, equivalents, time, catalyst loading), discrete or continuous bounds, encoded categoricals. The same Pydantic model the backend's ConditionDomain consumes.
Emits
design_space · variable_bounds · categoricals
Hard limits from Regulatory Hub auto-load: ICH Q3C residual-solvent ceilings, Q3D PDE limits, M7 nitrosamine alerts. Plus safety + cost guardrails from your operational profile.
Emits
objective_profile · cost · safety · regulatory_priors
Bayesian acquisition over a Gaussian-process surrogate. Expected Improvement (single objective), q-Noisy Expected Hypervolume Improvement (multi-objective batch), upper confidence bound for exploration. Reproducible — same seed, same batch.
Emits
recommendation_batch · acquisition_score · expected_improvement
Execute the proposed batch in the lab (manual) or on an autonomous platform (closed-loop). Recipe + parameters pinned by recipe-hash; cross-modal evidence pulled in automatically when the batch finishes.
Emits
run_id · recipe_hash · operator · timestamp
Spectroscopy evidence lands automatically. Yield, purity, selectivity, impurity profile, residual solvent — all per-condition, all hyperlinked to source spectra.
Emits
yield · purity · impurity_set · ee · spectra_uris
Surrogate refit with the new observation. Posterior mean + variance over the design space recompute. Constraint-aware: violated points get masked out before the next proposal.
Emits
posterior · constraint_mask · pareto_set
Continue, stop, or pivot. Stopping rules built in: hypervolume convergence, budget exhaustion, hyper-parameter saturation. Human reviewer signs every campaign decision into the audit ledger.
Emits
decision · rationale · reviewer · audit_event
At a glance
┌─► Define design space ──► Constrain (regulatory + safety + cost)
│ │
│ ▼
Decide (stop / pivot / continue) Propose (acquisition over GP surrogate)
▲ │
│ ▼
Update surrogate posterior ◄── Measure ◄── Run (manual or closed-loop)
with constraint mask (spectroscopy auto-attaches)Methods we ship
Switch regimes by argument — the design-space + objective + constraint contract stays identical across all four. The backend uses run_bayesian_optimization() with the same acquisition_score + expected_improvement fields.
Single-objective · model-based
Pareto-frontier · batch-aware
Uncertainty-driven · explore + exploit
Robot-driven · continuous campaign
Use cases shipped
Each is a typed pipeline you parameterise — not a bespoke build. The same acquisition engine drives every template; the differences are in design-space encoding and objective weighting.
Hit-finding across solvent / base / catalyst combinatorial space. Discrete-categorical Bayesian search with diversity-aware proposals.
Inputs
Categorical design space · screening budget
Outputs
Top-N hits · uncertainty bounds · suggested follow-up
Continuous-variable optimisation over temperature, equivalents, residence time. Multi-objective when selectivity matters as much as yield.
Inputs
Continuous bounds · objective weights
Outputs
Pareto frontier · best-by-objective conditions
Regulatory-Hub impurity limits become hard constraints. Optimiser proposes only conditions predicted to clear ICH Q3A/Q3B thresholds.
Inputs
Impurity limits from regulatory · prior runs
Outputs
Compliant conditions · constraint-aware ranking
Cost surrogate co-trained alongside yield. Proposes the cheapest condition that still hits the objective — useful for late-stage process work.
Inputs
Reagent unit costs · time budget · operator load
Outputs
Cost-Pareto frontier · sensitivity report
Optimises for robustness at scale: temperature insensitivity, mixing-time tolerance, work-up reproducibility. Variance-weighted objective.
Inputs
Lab-scale data · scale-up risk profile
Outputs
Robust conditions · sensitivity heatmap
HPLC method dev, work-up optimisation, crystallisation polymorph search. Same engine, different objectives — purity + recovery + crystal habit.
Inputs
Design variables · purity targets
Outputs
Validated method · Q2(R2)-ready parameters
Anatomy of a campaign
The campaign below is the worked example threading through every module page — acetic-acid impurity suppression. Round 4 violates the Q3C constraint mid-flight; Round 5's acquisition mask routes around it.
bayesian_optimization_run · run_4f7a · 5 rounds · convergence reached
| Round | Acquisition | Proposed | Measured | Status |
|---|---|---|---|---|
| R01 | Latin Hypercube · seed | T 25 °C · 1.2 eq · DCM · 4 h | yield 41% · imp 3.2% · cost low | Explore |
| R02 | EI · α 0.84 | T 50 °C · 1.5 eq · DCM · 6 h | yield 67% · imp 1.8% · cost low | Exploit |
| R03 | qNEHVI batch · k=3 | T 65 °C · 1.5 eq · MeCN · 8 h | yield 74% · imp 1.1% · cost mid | Exploit |
| R04 | UCB · β 1.6 | T 80 °C · 2.0 eq · MeCN · 4 h | ABORT · imp 5.8% > Q3C cap | Constraint |
| R05 | EI · constrained | T 60 °C · 1.8 eq · MeCN · 6 h | yield 81% · imp 0.7% · cost mid | Best so far |
Yield trajectory · convergence after constraint mask
R01 ████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ 41 % explore R02 █████████████░░░░░░░░░░░░░░░░░░░░░░░░░░░ 67 % exploit R03 ██████████████░░░░░░░░░░░░░░░░░░░░░░░░░░ 74 % exploit R04 ██████████░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░ ABORT Q3C limit exceeded R05 ████████████████░░░░░░░░░░░░░░░░░░░░░░░░ 81 % best · constraint-aware Pareto best 81 % yield · 0.7 % impurity (Q3C compliant) · mid cost Stop reason hypervolume Δ < 0.01 over last 2 rounds
The honest comparison
Trial-and-error campaigns aren't bad — they're under-instrumented. ReactionIQ keeps the chemist as the decision-maker but moves the experiment-selection step from gut to acquisition function.
| Dimension | Trial-and-error | ReactionIQ |
|---|---|---|
| Experiment selection | today Intuition + last-week's lab meeting + grad-school heuristics | with ReactionIQ Acquisition-function-driven · Expected Improvement over the live surrogate |
| Constraint handling | today Hope the operator remembers the impurity limit · catch in QC later | with ReactionIQ Hard constraint at proposal time · violated points masked before the optimiser ever sees them |
| Batch parallelism | today One reaction at a time · serialised round-trip on the bench | with ReactionIQ qNEHVI batch proposes N diverse runs · plate-aware suggestions |
| Replay 6 months later | today Lab notebook + memory · 'we tried that, didn't work, can't remember why' | with ReactionIQ Recipe-hash replay · same surrogate, same posterior, same proposal — forever |
| Stopping decision | today Run until budget runs out · or until someone gets frustrated | with ReactionIQ Hypervolume convergence · explicit stopping criteria · auditable decision |
| Cross-module evidence | today Yield in a spreadsheet · impurity in a PDF · cost in an email | with ReactionIQ One typed record per round · spectra hyperlinked · cost + impurity + audit entry together |
Three pillars, one loop
Picking up the cross-module worked example from Spectroscopy + Regulatory Hub — here's the role ReactionIQ plays when the impurity threshold is crossed mid-campaign.
Round 4 shows acetic acid impurity at 2.10 ppm climbed to 5.8% — over the ICH Q3C Class 3 informational threshold for this drug substance.
Constraint flips from 'informational' to 'hard limit' for subsequent rounds. ReactionIQ receives the new constraint vector via the typed cross-module API.
Surrogate masks the violating region. Round 5 acquisition function only proposes conditions predicted to keep acetic acid below 2%. Result: 81% yield, 0.7% impurity.
Cross-module handoff captured as a single audit_event with full provenance. Inspector can replay every decision from FID hash to recipe update.
Trust & reproducibility
Every campaign is replayable from any prior date. Every proposal is acquisition-attributed. Every pivot is signed. The surrogate is a tool — the chemist is the decision-maker.
Seed-reproducible campaigns
Same design space + same seed + same observations = same proposals. Forever. Recipe-hash-linked surrogate state pinned per round.
Constraint-aware by design
Regulatory limits, safety thresholds, and cost ceilings enter the optimiser as hard constraints — not soft hints. No proposal ever violates a known limit.
Human signoff per pivot
AI proposes batches. Humans approve campaigns. Every pivot (stop, continue, escalate) is signed and recorded with reviewer + role + rationale.
Safety abort wired live
Closed-loop campaigns include live sensor streams. Out-of-bounds temperature / pressure / off-gas triggers immediate abort + ledger entry.
Spectra linked per round
Every measured outcome carries the SpectraCheck SHA-256 of the source spectrum + the regulatory verdict + the cost line — one row, full provenance.
Campaign replay forever
Re-derive any historic Pareto front, any historic proposal, any historic decision from any prior date. Bit-identical, recipe-hash-pinned.
Pick a reaction where you've already run 30+ conditions and can't tell what to try next. We'll show you the Pareto frontier ReactionIQ would propose — with your regulatory constraints already enforced.