ReactWise - AI co-pilot for wet lab chemistry

TL;DR: ReactWise aims to accelerate and automate chemical process development by equipping wet lab chemists with the power of data-driven optimization and robotic execution of experiments.

The Problem

The discovery of novel pharmaceuticals is one of our most important weapons in fighting disease. However, the drug development pipeline is often held up for many months during the design of chemical processes to manufacture these drugs at scale, delaying FDA trials and lengthening the time until drug launch. Designing chemical processes involves the identification of suitable parameters such as catalyst/temperature/solvent. Currently, process development is often done via tedious trial-and-error experimentation (slow) or exhaustive screening (expensive and wasteful).

Our Approach

In our research, we have developed algorithms for chemical process optimization that leverage transfer learning and Bayesian optimization. We validated the algorithms in the wet lab, showing an up to 95% reduction in experimental burden and cost when compared to exhaustive screening. We have made our approaches accessible to human experimentalists through our user-friendly no-code software platform, and to automated laboratory equipment with our API.

Our Background

We (Daniel and Alexander) recently completed our PhDs in Machine Learning for Chemistry at the University of Cambridge. We built an automated lab to validate our optimization strategies during our research and are now working to accelerate and automate chemistry & biotech.

Our Ask

• We are keen to talk to innovation managers and labheads at pharmaceutical and biotech companies who
  • Work with experimentalists who would benefit from our no-code optimization platform
  • Want to build an automated lab but are not sure where to start
• We are also interested in speaking with equipment manufacturers so we can integrate our software with their hardware

Click here to book a demo with us, or email us via info@react-wise.com.