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- W4281389916 abstract "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" @default.
- W4281389916 created "2022-05-25" @default.
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- W4281389916 date "2022-05-23" @default.
- W4281389916 modified "2023-10-16" @default.
- W4281389916 title "Chemists debate machine learning’s future in synthesis planning and ask for open data" @default.
- W4281389916 doi "https://doi.org/10.1021/cen-10018-feature2" @default.
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