Cheminformatics in Natural Product-based Drug Discovery

Overview

Journal Mol Inform

Specialties Biochemistry
Biology
Chemistry
Molecular Biology

Date 2020 Jul 30

PMID 32725781

Citations 39

Authors

Ya Chen

Johannes Kirchmair

Affiliations

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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.

Citing Articles

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Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.

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Effectiveness of molecular fingerprints for exploring the chemical space of natural products.

Boldini D, Ballabio D, Consonni V, Todeschini R, Grisoni F, Sieber S J Cheminform. 2024; 16(1):35.

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