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When machine learning meets molecular synthesis
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DOI：10.1016/j.trechm.2022.07.005.png)
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The recent synergy of machine learning (ML) with molecular synthesis has emerged as an increasingly powerful platform in organic synthesis and catalysis. This merger has set the stage for key advances in inter alia reaction optimization and discovery as well as in synthesis planning. The creation of predictive ML models relies on chemical databases, molecular descriptors, and the choice of the ML algorithms. Chemical databases provide a crucial support of chemical knowledge contributing to the development of an accurate and generalizable ML model. Molecular descriptors translate the chemical structure into digital language, so that substrates or catalysts in molecular synthesis and catalysis are represented in a numerical fashion. ML algorithms achieve an effective map-ping between the molecular descriptors and the target properties, enabling an efficient prediction based on readily available or calculated descriptors. Herein, we highlight the key concepts and approaches in ML and their major potential to-wards molecular synthesis with emphasis in catalysis, pointing out additionally the most successful cases in the field.
Keyword:
CHEMISTRY INFORMER LIBRARIES
LIGAND KNOWLEDGE-BASE
ORGANIC-REACTIVITY
NEURAL-NETWORKS
PREDICTION
NUCLEOPHILICITY
SYSTEM
SCALES
OPTIMIZATION
MECHANISMS
期刊
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13.6
论文数:
825
被引数:
5.7K
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