Biological Dark Matter Exploration using Data Mining for the Discovery of Antimicrobial Natural Products^{[ # ]}

Abstract

The discovery of novel antimicrobials has significantly slowed down over the last three decades. At the same time, humans rely increasingly on antimicrobials because of the progressive antimicrobial resistance in medical practices, human communities, and the environment. Data mining is currently considered a promising option in the discovery of new antibiotics. Some of the advantages of data mining are the ability to predict chemical structures from sequence data, anticipation of the presence of novel metabolites, the understanding of gene evolution, and the corroboration of data from multiple omics technologies. This review analyzes the state-of-the-art for data mining in the fields of bacteria, fungi, and plant genomic data, as well as metabologenomics. It also summarizes some of the most recent research accomplishments in the field, all pinpointing to innovation through uncovering and implementing the next generation of antimicrobials.

Key words

genome exploration - data mining - metabologenomics - antimicrobial resistance - natural products

^# Dedicated to Professor Dr. A. Douglas Kinghorn on the occasion of his 75th birthday.

Publication History

Received: 21 December 2021

Accepted after revision: 03 March 2022

Article published online:
13 June 2022

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