'This algorithm may help find new antibiotics, cancer drugs'

In the search for new antibiotics and cancer 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 means of searching vast repositories of compounds produced by microbes.

Researchers, including those from the Carnegie Mellon University in the US, were able to identify known compounds within the repository and eliminate them from further analysis.

They focussed on the unknown variants that might potentially be better or more efficient antibiotics, anticancer drugs or other pharmaceuticals.

In just a week, running on 100 computers, the algorithm, called Dereplicator+, identified over 5,000 promising, unknown compounds that merit further investigation, said Hosein Mohimani, an assistant professor at Carnegie Mellon University.

In the past, mass spectrometry data repositories have been underused because it was difficult to search through them and because those efforts to date have been plagued by high rates of rediscovery of known compounds, researchers said.

"It is unbelievable how many times people have rediscovered penicillin," Mohimani said.

Analysing the compounds' mass spectra -- essentially, a measurement of the masses within a sample that has been ionised -- is a relatively inexpensive way of identifying possible 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 analyse the larger number of complex compounds that have entangled structures and numerous loops and branches, the researchers developed a method for predicting how a mass spectrometer would break apart the molecules.

Beginning with the weakest rings, the method simulated what would happen as the molecules came apart.

Using 5,000 known compounds and their mass spectra, they trained a computer model that could then be used to predict how other compounds would break down.

Mohimani said Dereplicator+ not only can identify known compounds that don't need to be investigated further, but it can also find less common variants of the known compounds that likely would go undetected within a sample.