Computational tools for the prediction of site- and regioselectivity of organic reactions.

• Tác giả: Michele Assante, Magnus J Johansson, Kjell Jorner, Mikhail Kabeshov, Per-Ola Norrby, Lukas M Sigmund
• Ngôn ngữ: eng
• Thông tin xuất bản: England : Chemical science , 2025
• Bộ sưu tập: NCBI
• ID: 697427

Tóm tắt

 The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational models for regio- and site-selectivity prediction, and the introduction of machine learning to the chemical sciences within the past decade has added a whole new dimension to these endeavors. This review article walks through the currently available predictive tools for regio- and site-selectivity with a particular focus on machine learning models while being organized along the individual reaction classes of organic chemistry. Respective featurization techniques and model architectures are described and compared to each other
  applications of the tools to critical real-world examples are highlighted. This paper aims to serve as an overview of the field's