Sunthetics

"After a successful retrospective validation, J-Star Research Scientists were able to reach a 64% reduction in experiments for the “optimization” of crystallization conditions using SuntheticsML proprietary technology, compared to traditional statistical design of experiments.

SuntheticsML is a useful and easy to use platform, which automatically builds and optimizes machine learning algorithms to guide experimental work towards optimal operation conditions."

- Dr. Yuriy Abramov Executive Director of Computational Chemistry and Data Science J-Star Research

"During the exploration of new synthetic methods, the Sunthetic platform allowed us to rapidly optimize yield and selectivity, and more importantly to determine the relative impact of each parameter. The real asset of the platform relies on 1. Its user-friendliness, 2. The speed of calculation (less than a minute), and 3. The availability and support of the Sunthetic team.

The algorithm took us to sets of reaction conditions we would never have thought worthy of testing – one of which turned out to be the optimum set of conditions!"

- Prof. Catherine S. J. Cazin Center for Sustainable Chemistry Ghent University