In Computational and structural biotechnology journal
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.
Lim Peng Ken, Julca Irene, Mutwil Marek
2023
Encoding reactome data, Neural-network encoders, Plant specialized metabolism, Predicting enzyme promiscuity, Predicting reaction-feasibility, Reactome data-mining, Retrobiosynthesis, Supervised machine learning
