Compare SaaS Products

We are building the next generation of deep-learning models for enzyme design to slash the cost of chemical manufacturing. The $6 trillion chemical industry is flawed: It produces 20% of industrial greenhouse gases, and is responsible for 15% of global energy usage. Enzymes offer a far more sustainable alternative to chemical synthesis and have already revolutionised how a select few chemicals are produced. The problem is each enzyme takes years of trial and error to develop. Our enzyme models learn the principles of catalysis, allowing us to design enzymes for each reaction, in days not years. We currently have $4.3M of LOIs and have completed our first $20k contract.

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 required experiments and cost compared to exhaustive screening. We have made our approaches accessible to chemists through our user-friendly no-code software platform and to automated laboratory equipment with our API.