Managing macrolactones

New database could aid large-molecule drug discovery and research

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RALEIGH, N.C.—Small molecules dominate so much of drug discovery research efforts that we don’t get to highlight the larger ones nearly enough in the pages of DDN. So it was welcome news from researchers at North Carolina State University (NC State) and Collaborations Pharmaceuticals when they reported that they have created a free-to-use database of 14,000 known macrolactones—large molecules used in drug development—that contains information about the molecular characteristics, chemical diversity and biological activities of this structural class.
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.
The new database, called MacrolactoneDB, fills a knowledge gap concerning these molecules and could serve as a useful tool for future drug discovery.
“Macrolactones are titanic molecules; their size presents challenges to researchers who may want to work with them,” explained Sean Ekins, CEO of Collaborations Pharmaceuticals 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 percent of the macrolactone compounds she curated had biological data associated with them.
Zin, Ekins and Gavin Williams—an NC State associate professor of chemistry—conducted cheminformatics analyses of the macrolactones’ molecular properties and developed 91 descriptors to better characterize the molecules.
Following that up, they picked out targets of interest for some of the macrolactones—specifically, malaria, hepatitis C and T cells—and then employed 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 said. “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.”
Added Ekins: “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. 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.”
The researchers’ report appeared under the title “Cheminformatics Analysis and Modeling with MacrolactoneDB” recently in Scientific Reports, and was supported by the National Institutes of Health. Zin received additional funding from the American Association of University Women and an NC State Graduate Research Assistantship.
Keeping up with the theme of large molecules, there is also news recently out of Dublin-based Research and Markets of a new report from Roots Analysis, titled “In Silico/Computer-Aided Drug Discovery Services Market: Focus on Large Molecules.” Among the highlights of the report:
  • Over 30 percent of the total cost invested in developing a new drug can be saved by utilizing in-silico services. However, this is highly variable and is dependent upon the nature of drug discovery projects.
  • The process of drug development, beginning from the discovery of a pharmacological lead to its commercial launch, is estimated to take around 10 to 15 years, involving capital investments in the range of $4 billion to $10 billion.
  • Over time, the complexities of drug discovery have increased, and this is especially true for large molecules, which are inherently more complex than conventional small-molecule drugs. As a result, there has been a direct rise in overall research and development expenditure in the pharmaceutical/biotechnology sector.
  • During the last several years, several computational tools have been developed and introduced for enabling the identification, selection and optimization of pharmacological lead candidates. Currently, there are several in-silico approaches available for the drug discovery process alone, such as structure-based drug design, fragment-based drug discovery and ligand-based drug discovery.
The report is available via the Research and Markets website at

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