Skip to content

Case Study 2 — Process Chemistry

  • Customer profile
  • Challenge: manual reaction screening
  • Solution: Bayesian optimization with regulatory constraints
  • Result: fewer experiments, higher yield, impurity below threshold
  • Quote from process chemistry lead
  • Baseline number of experiments per optimization campaign.
  • Time from campaign start to selected condition before MolTrace.
  • Number of MolTrace iterations to reach the accepted condition.
  • Yield, selectivity, impurity, and safety constraints before and after.
  • Decision points where the chemist accepted, modified, or rejected recommendations.

The process chemistry lead must approve every metric and quote before publication.