Machine learning-guided strategies for reaction conditions design and optimization

• Lung-Yi Chen and
• Yi-Pei Li

Beilstein J. Org. Chem. 2024, 20, 2476–2492, doi:10.3762/bjoc.20.212

• efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry. Keywords: data preprocessing; reaction conditions prediction; reaction data mining; reaction optimization; reaction representation; Introduction Machine learning (ML) techniques have been widely

• models can reproduce patent-derived pathways for known compounds, and even suggest more diverse and efficient alternatives [5][6][7][8]. Building upon the retrosynthesis, the reaction conditions prediction models can help in identifying appropriate conditions for each step, ensuring compatibility with

• chemical scenarios. Finally, we summarize the progress in this field and underline the remaining challenges in the area of reaction condition design. Review Reaction data collection and preprocessing One of the major challenges in building ML models for global reaction conditions prediction is the data