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

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

‍

Most labs today can execute experiments faster than ever. Throughput is up, automation is cheaper, and generating data is rarely the limiting step anymore.

‍

The bottleneck shows up one step later: interpretation.

‍

And in practice, the hardest part of interpretation often isn’t the continuous dimensions like temperature, concentrations, or time. It’s the categorical choices that dominate the chemistry: solvents, bases, and catalysts.

‍

Those choices carry most of the chemical “meaning” in a workflow. But they’re also the easiest to reduce to labels in a spreadsheet. When you do that, you end up with conclusions that are hard to trust and even harder to transfer.

‍

“Solvent B worked best” is not an insight.

The insight is why it worked best.

‍

Was it driven by its polarity, miscibility, solubility, or something else entirely?

‍

If you can’t translate categories into properties, the learning stays stuck in that one campaign. Teams run more experiments, swap one solvent for another, and generate more data. But they don’t build understanding that generalizes.

‍

So the real bottleneck isn’t running the next experiment. It’s interpreting the last one in a way that makes the outcome reusable.

‍

Here is how we’re addressing this at @ReactWise. 

‍

We bring molecular insights into the hands of chemists by encoding categorical choices with chemistry-aware descriptors, and then making those drivers interpretable with SHAP visualizations of the underlying features. 

The goal isn’t just to show what won, but to reveal why - so teams can move faster without losing understanding.