New medicines for future healthcare are urgently sought for, and it comes at no surprise that molecules of natural origin have emerged as valuable starting points for drug discovery. Living organisms produce all kinds of bioactive compounds for regulation of physiological processes and host defence, just to name some prominent biological activities that are highly attractive for chemical and pharmaceutical discovery.
Starting from metabolomics data, Yuki Ohtana from the Nara Institute of Science and Technology, Ikoma, Japan, and an international team of coworkers investigated relationships between three-dimensional chemical structures and their potential biological activities. The researchers compiled a large set of secondary metabolites from different species for analysis with an elaborate data mining approach. Key to success was a series of molecular “omics” databases, a network algorithm connecting chemical structure with biological activity, and statistical confidence assessment of the computationally generated associations.
The results of this study present a way to scrutinize “Big Data” in the molecular life sciences and extract information for future chemical and drug discovery.
- Clustering of 3D-Structure Similarity Based Network of Secondary Metabolites Reveals Their Relationships with Biological Activities,
Yuki Ohtana, Azian Azamimi Abdullah, Md. Altaf-Ul-Amin, Ming Huang, Naoaki Ono, Tetsuo Sato, Tadao Sugiura, Hisayuki Horai, Yukiko Nakamura, Aki Morita (Hirai), Klaus W. Lange, Nelson K. Kibinge, Tetsuo Katsuragi, Tsuyoshi Shirai and Shigehiko Kanaya,
Mol. Inf. 2014, 33, 790–801.
This article was selected by the editors as the Molecular Informatics 2014 Best Paper.
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