Lim Peng Ken, Julca Irene, Mutwil Marek
School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
Comput Struct Biotechnol J. 2023 Jan 18;21:1639-1650. doi: 10.1016/j.csbj.2023.01.013. eCollection 2023.
The immense structural diversity of products and intermediates of plant specialized metabolism (specialized metabolites) makes them rich sources of therapeutic medicine, nutrients, and other useful materials. With the rapid accumulation of reactome data that can be accessible on biological and chemical databases, along with recent advances in machine learning, this review sets out to outline how supervised machine learning can be used to design new compounds and pathways by exploiting the wealth of said data. We will first examine the various sources from which reactome data can be obtained, followed by explaining the different machine learning encoding methods for reactome data. We then discuss current supervised machine learning developments that can be employed in various aspects to help redesign plant specialized metabolism.
植物特殊代谢（特殊代谢产物）的产物和中间体具有巨大的结构多样性，使其成为治疗药物、营养物质和其他有用材料的丰富来源。随着生物和化学数据库中可获取的反应组数据的快速积累，以及机器学习的最新进展，本综述旨在概述如何通过利用这些丰富的数据，使用监督机器学习来设计新的化合物和途径。我们将首先研究可获取反应组数据的各种来源，然后解释反应组数据的不同机器学习编码方法。接着，我们将讨论当前可用于各个方面以帮助重新设计植物特殊代谢的监督机器学习进展。
