Chemists debate machine learning’s future in synthesis planning and ask for open data
special to C EN Fernando Gomollón-Bel · C&EN Global Enterprise · 2022
In March, a paper in the Journal of the American Chemical Society sparked a heated Twitter debate on the value of machine learning for predicting optimal reaction pathways in synthetic chemistry. The authors argue that certain data-driven algorithms capture only trends that already exist in the chemical literature, thus failing to reach new, creative conclusions ( J. Am. Chem. Soc. 2022, DOI: 10.1021/jacs.1c12005 ). But other experts disagree and advocate for the use of such algorithms . The key to success, they say, is for researchers to ask computers the right questions, establish good benchmarks for machine learning, and promote open-data initiatives. Bartosz A. Grzybowski of the Institute for Basic Science in South Korea co-led the study. He and his group have created software, such as Chematica ( now Synthia ) and Allchemy , that use the basic rules of chemistry to predict multistep synthetic pathways and complex biosynthetic ones