ReactWise

Our Mission: We aim 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 which 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 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 studies, and are now working to accelerate and automate chemistry & biotech.