Synthesizing natural products with a computational step saver

Subtle differences in calculated energy landscapes impact synthetic planning.
Subtle differences in calculated energy landscapes impact synthetic planning. (Image credit: Joshua Zweig)

Yale researchers have slashed the number of steps needed to synthesize two natural anticancer products, using a novel approach to computational analysis in organic chemistry.

The new strategy offers the potential to find quicker, more effective chemical routes for synthesizing a host of natural products. The researchers say it may be particularly useful to scientists in the planning stages of synthesizing small molecules.

In planning the synthesis of a molecule, there are often questions about feasibility that cannot be answered through either a literature search or through qualitative chemical analysis,” said Yale chemistry professor Timothy Newhouse, who led the research.

Instead, Newhouse and colleagues Daria Kim and Joshua Zweig used density functional theory (DFT) calculations to evaluate and prioritize chemical pathways — each pathway seemingly equivalent — in order to plan synthesis. DFT is a method of computational quantum mechanical modelling that examines the structural, magnetic, and electronic properties of a material.

In this case, the Yale team used DFT to synthesize the molecule paspaline A in only nine steps. Previous work by other research groups needed 25 or 27 steps to achieve synthesis. The Yale group also reported a 13-step path to synthesize the compound emindole PB, which had never been synthesized.

Our synthesis, enabled by computational chemistry, is about a third of the number of synthetic operations as previous approaches. A more efficient synthesis allows for the production of a greater amount of material, and also a laboratory process that can more easily be used to produce synthetic analogs,” said Kim, who is lead author of the study.

The study is published on the open-data research site ChemRxiv.

Funding for the research comes from Yale University, the Sloan Foundation, and the National Science Foundation.


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