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New macrolactone database could aid drug discovery, research

Date:
April 21, 2020
Source:
North Carolina State University
Summary:
Researchers have created a free-to-use database of 14,000 known macrolactones -- large molecules used in drug development -- which contains information about the molecular characteristics, chemical diversity and biological activities of this structural class.
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Researchers from North Carolina State University and Collaborations Pharmaceuticals have created a free-to-use database of 14,000 known macrolactones -- large molecules used in drug development -- which contains information about the molecular characteristics, chemical diversity and biological activities of this structural class. The database, called MacrolactoneDB, fills a knowledge gap concerning these molecules and could serve as a useful tool for future drug discovery.

Macrolactones are molecules with at least 12 atoms composing their ring-like structure. Among many useful characteristics, macrolactones' ability to bind to difficult protein targets makes them suitable for antiviral, antibiotic, antifungal and antiparasitic drugs. However, their size and complicated structure make them difficult to synthesize.

"Macrolactones are titanic molecules -- their size presents challenges to researchers who may want to work with them," says Sean Ekins, CEO of Collaborations Pharmaceuticals, member of NC State's Comparative Medicine Institute, entrepreneur in residence at UNC-Chapel Hill's Eshelman School of Pharmacy and corresponding author of the research. "We wanted to address that issue by creating a publicly available database of these molecules and their properties."

NC State graduate student and first author of the paper Phyo Phyo Zin mined 13 public databases for 14,000 known macrolactones, compiling them into MacrolactoneDB. Only 20% of the macrolactone compounds she curated had biological data associated with them.

Zin, Ekins, and NC State Associate Professor of Chemistry Gavin Williams conducted cheminformatics analyses of the macrolactones' molecular properties and developed 91 descriptors to better characterize the molecules. The researchers then looked at three targets of interest for some of the macrolactones -- specifically malaria, hepatitis C and T cells -- and used machine-learning techniques to understand the structure-activity relationship between the macrolactones and these targets.

"We know that macrolactone drugs are effective, but there's a lot we don't know about what makes a good one," Williams says. "That's why we set out to do this research. We found that it is possible to utilize machine learning with these molecules, and improving our analysis and description of macrolactones will improve prediction models going forward."

"Anyone interested in these molecules or in drug development utilizing macrolactones now has a user-friendly database where everything is accessible and in one location," Ekins says. "Researchers can ask questions about what makes a particular macrolactone molecule well-suited for a particular biological application.

"Hopefully MacrolactoneDB will help us to understand this diverse class of molecules, and move forward in creating new ones."


Story Source:

Materials provided by North Carolina State University. Note: Content may be edited for style and length.


Journal Reference:

  1. Phyo Phyo Kyaw Zin, Gavin J. Williams, Sean Ekins. Cheminformatics Analysis and Modeling with MacrolactoneDB. Scientific Reports, 2020; 10 (1) DOI: 10.1038/s41598-020-63192-4

Cite This Page:

North Carolina State University. "New macrolactone database could aid drug discovery, research." ScienceDaily. ScienceDaily, 21 April 2020. <www.sciencedaily.com/releases/2020/04/200421112553.htm>.
North Carolina State University. (2020, April 21). New macrolactone database could aid drug discovery, research. ScienceDaily. Retrieved February 25, 2024 from www.sciencedaily.com/releases/2020/04/200421112553.htm
North Carolina State University. "New macrolactone database could aid drug discovery, research." ScienceDaily. www.sciencedaily.com/releases/2020/04/200421112553.htm (accessed February 25, 2024).

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