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Machine learning the ropes: principles, applications and directions in synthetic chemistry.
Chemical Society Reviews ( IF 46.2 ) Pub Date : 2020-07-16 , DOI: 10.1039/c9cs00786e
Felix Strieth-Kalthoff 1 , Frederik Sandfort 1 , Marwin H S Segler 1 , Frank Glorius 1
Affiliation  

Machine learning (ML) has emerged as a general, problem-solving paradigm with many applications in computer vision, natural language processing, digital safety, or medicine. By recognizing complex patterns in data, ML bears the potential to modernise the way how many chemical challenges are approached. In this review, an introduction to ML is given from the perspective of synthetic chemistry: starting from the fundamentals regarding algorithms and best-practice workflows, the review covers different applications of machine learning in synthesis planning, property prediction, molecular design, and reactivity prediction. In particular, different approaches of representing and utilizing organic molecules will be discussed – providing synthetic chemists both with the understanding and the tools required to apply machine learning in the context of their research, and pointers for further studying.

中文翻译:

机器学习的绳索:合成化学的原理,应用和指导。

机器学习(ML)已经成为解决问题的通用范式,在计算机视觉,自然语言处理,数字安全或医学中有许多应用。通过识别数据中的复杂模式,机器学习有潜力实现应对多种化学挑战的方式的现代化。在这篇综述中,从合成化学的角度对机器学习进行了介绍:从有关算法和最佳实践工作流的基础开始,该综述涵盖了机器学习在合成计划,特性预测,分子设计和反应性预测中的不同应用。尤其是,
更新日期:2020-09-01
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