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Current Trends in Translational Bioinformatics

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Front. Genet. | doi: 10.3389/fgene.2019.00368

Informatics and computational methods in natural product drug discovery: A review and perspectives

 Joseph D. Romano1, 2, 3, 4 and Nicholas P. Tatonetti1, 2, 3, 4*
  • 1Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, United States
  • 2Department of Systems Biology, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, United States
  • 3Department of Medicine, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, United States
  • 4Data Science Institute, Fu Foundation School of Engineering & Applied Science, Columbia University, United States

The discovery of new pharmaceutical drugs is one of the preeminent
tasks---scientifically, economically, and socially---in biomedical
research. Advances in informatics and computational biology have
increased productivity at many stages of the drug
discovery pipeline. Nevertheless, drug discovery has slowed, largely due
to the reliance on small molecules as the primary source of novel
hypotheses.

In recent years, it has become apparent that natural products (such
as plant metabolites, animal toxins, and immunological components)
comprise a vast and diverse source of bioactive compounds, some of which
are supported by thousands of years of
traditional medicine. However, natural
products possess unique characteristics that distinguish them from
traditional small molecule drug candidates, requiring new methods
and approaches for assessing their therapeutic potential.

In this review, we investigate a number of state-of-the-art
techniques in bioinformatics, cheminformatics, and knowledge
engineering for data-driven drug discovery from natural products. We
focus on methods that aim to bridge the gap between
traditional small-molecule drug candidates and different classes of
natural products. We also explore the current informatics knowledge
gaps and other barriers that need to be overcome to fully leverage
these compounds for drug discovery. Finally, we
conclude with a `road map' of research priorities that seeks to
realize this goal.

Keywords: Drug Discovery, methods, Cheminformatics, bioinformatics, ontologies, translation, Natural Products

Received: 12 Dec 2018; Accepted: 05 Apr 2019.

Edited by:

Dana C. Crawford, Case Western Reserve University, United States

Reviewed by:

Brett K. Beaulieu-Jones, Harvard Medical School, United States
Ellen L. Palmer, Case Western Reserve University, United States
Harry Hochheiser, University of Pittsburgh, United States  

Copyright: © 2019 Romano and Tatonetti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Nicholas P. Tatonetti, Department of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University Irving Medical Center, New York City, 10027, New York, United States, npt2105@cumc.columbia.edu