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      Machine learning-assisted structure annotation of natural products based on MS and NMR data

      Author(s): 1 , 2 , 1 , 2
      Publication date (Electronic):
      Journal: Natural Product Reports
      Publisher: Royal Society of Chemistry (RSC)

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          Abstract

          This review presents a summary of the recent advancements in machine learning-assisted structure elucidation (MLASE) to establish the structures of natural products (NPs).

          Abstract

          Covering: up to March 2023

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          Most cited references141

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          Deep learning.

          Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
          Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
          Article 0 comments Cited 10282 times – based on 0 reviews     Review now
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            ImageNet classification with deep convolutional neural networks

            Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton (2017)
            Article 0 comments Cited 4036 times – based on 0 reviews     Review now
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              Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking.

              Mingxun Wang, Jeremy J. Carver, Vanessa V Phelan … (2016)
              The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.
              Article 0 comments Cited 1091 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Journal
                Journal ID (publisher-id): NPRRDF
                Title: Natural Product Reports
                Abbreviated Title: Nat. Prod. Rep.
                Publisher: Royal Society of Chemistry (RSC)
                ISSN (Print): 0265-0568
                ISSN (Electronic): 1460-4752
                Publication date Created: November 15 2023
                Publication date (Electronic): 2023
                Volume: 40
                Issue: 11
                Pages: 1735-1753
                Affiliations
                [1 ]State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China
                [2 ]University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China
                Article
                DOI: 10.1039/D3NP00025G
                PubMed ID: 37519196
                SO-VID: a7f29b4f-3369-4e85-a63b-9728205f784f
                Copyright © © 2023
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                http://rsc.li/journals-terms-of-use

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