Cheminformatics in Natural Product‐based Drug Discovery

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Abstract: This review seeks to provide a timely survey of the scope and limitations of cheminformatics methods in natural product‐based drug discovery. Following an overview of data resources of chemical, biological and structural information on natural products, we discuss, among other aspects, in silico methods for (i) data curation and natural products dereplication, (ii) analysis, visualization, navigation and comparison of the chemical space, (iii) quantification of natural product‐likeness, (iv) prediction of the bioactivities (virtual screening, target prediction), ADME and safety profiles (toxicity) of natural products, (v) natural products‐inspired de novo design and (vi) prediction of natural products prone to cause interference with biological assays. Among the many methods discussed are rule‐based, similarity‐based, shape‐based, pharmacophore‐based and network‐based approaches, docking and machine learning methods.

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: Online-Ressource

Language: Englisch

Bibliographic citation: Cheminformatics in Natural Product‐based Drug Discovery ; volume:39 ; number:12 ; year:2020 ; extent:16
Molecular informatics ; 39, Heft 12 (2020) (gesamt 16)

Creator: Chen, Ya
Kirchmair, Johannes

DOI: 10.1002/minf.202000171

URN: urn:nbn:de:101:1-2022062612343017711599

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:21 AM CEST

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Chen, Ya
Kirchmair, Johannes

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