Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-25030
Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Computational literature-based discovery for natural products research : current state and future prospects
Authors: Lardos, Andreas
Aghaebrahimian, Ahmad
Koroleva, Anna
Sidorova, Julia
Wolfram, Evelyn
Anisimova, Maria
Gil, Manuel
et. al: No
DOI: 10.3389/fbinf.2022.827207
10.21256/zhaw-25030
Published in: Frontiers in Bioinformatics
Volume(Issue): 2
Issue: 827207
Issue Date: 15-Mar-2022
Publisher / Ed. Institution: Frontiers Research Foundation
ISSN: 2673-7647
Language: English
Subjects: Literature-based discovery; Natural product; Text mining; Knowledge graph; Natural language processing; Swanson; Semantic integration; Ontology
Subject (DDC): 000: Generalities and science
006: Special computer methods
Abstract: Literature-based discovery (LBD) mines existing literature in order to generate new hypotheses by finding links between previously disconnected pieces of knowledge. Although automated LBD systems are becoming widespread and indispensable in a wide variety of knowledge domains, little has been done to introduce LBD to the field of natural products research. Despite growing knowledge in the natural product domain, most of the accumulated information is found in detached data pools. LBD can facilitate better contextualization and exploitation of this wealth of data, for example by formulating new hypotheses for natural product research, especially in the context of drug discovery and development. Moreover, automated LBD systems promise to accelerate the currently tedious and expensive process of lead identification, optimization, and development. Focusing on natural product research, we briefly reflect the development of automated LBD and summarize its methods and principal data sources. In a thorough review of published use cases of LBD in the biomedical domain, we highlight the immense potential of this data mining approach for natural product research, especially in context with drug discovery or repurposing, mode of action, as well as drug or substance interactions. Most of the 91 natural product-related discoveries in our sample of reported use cases of LBD were addressed at a computer science audience. Therefore, it is the wider goal of this review to introduce automated LBD to researchers who work with natural products and to facilitate the dialogue between this community and the developers of automated LBD systems.
URI: https://digitalcollection.zhaw.ch/handle/11475/25030
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: Life Sciences and Facility Management
Organisational Unit: Institute of Chemistry and Biotechnology (ICBT)
Institute of Computational Life Sciences (ICLS)
Appears in collections:Publikationen Life Sciences und Facility Management

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