Algorithmic Process Optimization (APO) for Pharmaceutical Development.
2025 Data Science and Modeling for Green Chemistry Award
The Merck and Sunthetics team, consisting of Kevin Stone, Daniela E. Blanco, Kaitlyn Brinza, Melodie Christensen, Shane Grosser, Yasser Khelalef, Abderrahman Lazizi, Andy Liaw, Spencer McMinn, Rafik Oulbsir, Victor Schultz, Ethan Tenison, César A. Urbina-Blanco, Ajit Vikram, and Yuting Xu, is awarded for their work, “Algorithmic Process Optimization (APO) for Pharmaceutical Development.” This technology makes use of state-of-the-art approaches in active learning, including Bayesian Optimization, to locate global optima in complex operational spaces that are expensive to evaluate experimentally. The Merck and Sunthetics team developed and demonstrated the APO technology allows for sustainable process design by minimizing material use and selecting non-toxic reagents, translating into reductions of the drug development costs. APO’s versatility allows it to tackle numeric, discrete, and mixed-integer optimization problems with at least 11 input parameters, supporting both serial and parallel experimentation. Its ability to handle multi-objective optimizations focusing on cost and material efficiency with notable performance shows promise for AI-powered design for optimized and more sustainable processes.
More about the Award
The Data Science and Modeling for Green Chemistry Award aims to recognize the research and development of computational tools that guide the design of sustainable chemical processes and the execution of green chemistry that demonstrates compelling environmental, safety, and efficiency improvements over current technologies in the pharmaceutical industry and its allied industrial partners.
This annual award is presented at the Green Chemistry & Engineering Confernece where presenters are invited to share their innovations.
