ReactWise Reaches 97.5% Yield and 1% Impurity in Pfizer Hydrogenation Case Study

In a case study with Pfizer, ReactWise reached 97.5% yield with 1% impurity in a hydrogenation process and discovered a critical parameter related to the temperature profile that materially influenced process performance. ReactWise describes the result as a major milestone showing measurable real-world impact for chemists facing complex multi-objective optimization. The challenge was to identify a temperature profile for a hydrogenation process that could maximize yield while minimizing impurity across a complex, highly interdependent design space. Using an iterative multi-objective Bayesian optimization workflow, ReactWise explored broad operating ranges, rapidly identified the variables that mattered most, and converged on the optimal region far more efficiently than traditional trial-and-error experimentation. The work optimized hold temperatures, hold times, temperature ramp rates and the broader time-temperature profile together. ReactWise says the case study highlights what an AI copilot for chemical process development can make possible: faster learning, smarter experimentation and better process outcomes.