Reaction Optimization Overview
Reaction Optimization recommends experiments by combining prior reaction evidence, measured outcomes, and constraints such as yield, selectivity, impurity level, and regulatory limits.
- Gaussian-process surrogate modeling in plain language
- Multi-objective optimization across yield, selectivity, and impurity profile
- Regulatory constraints from ICH thresholds
- Human acceptance, modification, or rejection of recommendations
Authoring plan
Section titled “Authoring plan”Interview the process chemist before finalizing this guide. Ask them to explain the workflow as if they were speaking to a smart non-statistician:
- What makes a good first experiment set?
- Which variables should be continuous, categorical, or fixed?
- What does the optimizer know after each iteration?
- When should a chemist reject a recommendation?
- Which impurity or safety constraints are non-negotiable?
The final page should explain Bayesian optimization without jargon: MolTrace builds a probabilistic model of the reaction landscape, estimates uncertainty, and recommends the next experiment that best balances likely improvement with learning value.
Recommendation card
Section titled “Recommendation card”The next-experiment recommendation card should show:
- Suggested temperature, concentration, catalyst, solvent, and time.
- Predicted yield, selectivity, and impurity profile.
- Uncertainty or confidence interval.
- Constraint checks and any ICH-linked warning.
- Accept, modify, reject, and rationale actions.