@inbook{5fd79d3b150f428e9253ec917ce1fe78,
title = "A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research",
abstract = "Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.",
keywords = "Biological Products/pharmacology, Chemistry, Pharmaceutical, Computational Biology, Drug Discovery, Machine Learning, Models, Molecular, Cheminformatics, Natural product, Virtual screening, Big data, Traditional medicine, Drug discovery",
author = "Benjamin Kirchweger and Rollinger, \{Judith M\}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.",
year = "2019",
doi = "10.1007/978-3-030-14632-0\_7",
language = "English",
isbn = "978-3-030-14631-3",
volume = "110",
series = "Progress in the Chemistry of Organic Natural Products",
pages = "239--271",
editor = "Kinhorn, \{A. Douglas\} and Heinz Falk and Simon Gibbons and Jun'ichi Kobayashi and Yoshinori Asakawa and Ji-Kai Liu",
booktitle = "Progress in the Chemistry of Organic Natural Products",
publisher = "Springer",
}