Cheminformatics in Natural Product‐based Drug Discovery

• Published in: Molecular informatics Vol. 39; no. 12; pp. e2000171 - n/a
• Main Authors: Chen, Ya, Kirchmair, Johannes
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.12.2020; John Wiley and Sons Inc
• Subjects: Biocompatibility; Biological Assay; Biological Products - analysis; Biological Products - chemistry; Cheminformatics; Computer Simulation; Drug Discovery; Humans; in silico methods; Informatics; Learning algorithms; Machine learning; Macromolecular Substances - chemistry; Natural products; Predictions; Product safety; Review; Reviews; Toxicity; databases; natural products; drug discovery; in silico methods; cheminformatics
• ISSN: 1868-1743; 1868-1751; 1868-1751
• DOI: 10.1002/minf.202000171

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.