Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.
Encoding reactome data
Neural-network encoders
Plant specialized metabolism
Predicting enzyme promiscuity
Predicting reaction-feasibility
Reactome data-mining
Retrobiosynthesis
Supervised machine learning
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2023
2023
Historique:
received:
27
10
2022
revised:
12
01
2023
accepted:
12
01
2023
entrez:
6
3
2023
pubmed:
7
3
2023
medline:
7
3
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 36874159
doi: 10.1016/j.csbj.2023.01.013
pii: S2001-0370(23)00013-2
pmc: PMC9976193
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1639-1650Informations de copyright
© 2023 The Author(s).
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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