What's new at ReactWise

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No-code vs low-code in the lab: Why we prioritize simplicity

“I’d love to use data-driven optimisation, but I don’t have the coding skills.”

The biggest product improvements rarely look shiny…

What if your machine learning model could ‘see’ chemistry like a chemist? The secret isn’t more data - it’s smarter descriptors.

We’ve just launched our new homepage for ReactWise! 🚀

From Lab Intuition to Machine Learning Algorithms: ReactWise’s Playbook for Faster Reaction Optimization

„Ich würde gerne datengetriebene Optimierung nutzen, aber mir fehlen die Programmierkenntnisse.“

I used to think our competition was another startup. I was wrong.

The most expensive mistake in the lab is a result you can’t explain afterward.

Today the hidden bottleneck isn’t experiments - it’s interpretation

In process development, the best optimization method is useless if the team can’t actually use it

Good optimization feels less like finding an answer and more like navigating uncertainty.

The most underrated skill in AI-driven R&D is knowing when not to automate

Process Optimization in 2026: Data, Sustainability, and Robustness

What surprised me most when watching chemists use AI - without us guiding them

Most ‘autonomous lab’ projects fail before the robot even moves.

Machine learning is only as good as the data behind it - at ReactWise we have taken this into our own hands.

In 2026, the competitive advantage will come from AI that actually runs end-to-end. From data to decision to execution.

We’re entering an era where “more data” is no longer an advantage - unless the workflows are ready for it.

Traditional Bayesian optimisation starts from zero every time. MemoryBO doesn’t.

Transfer learning is where Bayesian optimization gets interesting

What if chemical procedures worked like code? Think GitHub for chemistry.

Is the peer-review process slowing science down?