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New York, Oct. 3 (PTI) In the search for new antibiotics and anticancer drugs, scientists have developed a computer algorithm that reduces the chances of simply rediscovering known compounds.
The study, published in the journal Nature Communications, found a new way to search for vast repositories of compounds produced by microbes.
Researchers, including those at Carnegie Mellon University in the United States, were able to identify known compounds in the repository and eliminate them from further analysis.
They focused on unknown variants that may be better or more effective antibiotics, anticancer drugs or other pharmaceuticals.
In just one week, running on 100 computers, the algorithm, called Dereplicator +, has identified more than 5,000 unknown promising compounds, which deserve to be deepened, said Hosein Mohimani, an assistant professor at the university. Carnegie Mellon University.
In the past, mass spectrometry data repositories were underutilized due to the difficulty of tracking them and the efforts made to date by high levels of rediscovery of known compounds, the researchers explained.
"It's amazing how many times people have rediscovered penicillin," Mohimani said.
The analysis of the mass spectra of the compounds – essentially a measure of the masses of an ionized sample – is a relatively inexpensive way of identifying new pharmaceuticals.
However, existing techniques were largely limited to peptides, which have simple structures such as chains and loops.
"We were only looking at the tip of the iceberg," Mohimani said.
To analyze the largest number of complex compounds with entangled structures and many loops and branches, researchers have developed a method to predict how a mass spectrometer would divide molecules.
Starting with the weakest rings, the method simulated what would happen when the molecules separated.
Using 5,000 known compounds and their mass spectra, they formed a computer model that could then be used to predict the degradation of other compounds.
Mohimani said that Dereplicator + can not only identify known compounds that it is not necessary to pursue, but it can also look for less common variants of known compounds that would not likely pass through a sample.
This is published unedited PTI stream.
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