A 95% yield in the lab is a great result. It's not a robust process. Here's the difference.

A 95% yield in the lab is a great result. It's not a robust process. Here's the difference.

A process chemistry team recently came to us with exactly this situation. Bayesian optimization had done its job beautifully - 95% yield, impurity well below threshold, achieved in fewer experiments than anyone expected.

Then came scale-up. The yield dropped. The impurity profile shifted. And nobody could explain why - because the optimization had found the answer without building the understanding behind it.

Which parameter was driving the yield? What does a 3°C temperature deviation do to the impurity profile? What happens when mixing time changes at larger scale - as it inevitably does?

These aren't academic questions. They're what a regulatory submission requires. They're what a tech transfer to a CDMO depends on.

This is exactly why we've built explainability into @ReactWise as a core part of the workflow - not an afterthought.

You can see which parameters drive yield and which drive impurity. You can visualize robust operating regions - where your process remains stable even as conditions naturally vary. And you can ask direct questions: what happens to my impurity profile if temperature shifts by 3°C? What's the consequence of a concentration deviation at scale?

Kinetic models take this further - revealing the mechanism behind your optimal conditions and giving you the understanding that scale-up decisions actually require.

Because the goal was never just to find the best conditions. It was to understand them well enough to defend them, transfer them, and scale them with confidence.

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