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Cheminformatics in Natural Product‐based Drug Discovery
Author(s) -
Chen Ya,
Kirchmair Johannes
Publication year - 2020
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.202000171
Subject(s) - cheminformatics , chemical space , natural product , drug discovery , computer science , adme , pharmacophore , chemical similarity , virtual screening , in silico , chembl , visualization , biochemical engineering , computational biology , data science , machine learning , data mining , artificial intelligence , chemistry , bioinformatics , engineering , biology , cluster analysis , pharmacokinetics , biochemistry , gene , stereochemistry
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.