Machine learning identifies promising antibacterial ruthenium-based drug candidates

A machine learning model has been created that can identify ruthenium-based antibiotic drug candidates. With a small training set of just 288 antibacterial organometallic compounds, the algorithm scanned millions of structures, selecting the most active against resistant bacteria. A number of the most promising candidates was tested and showcased almost six times greater antibiotic activity than the training set .

Antibiotics have become ‘a cornerstone of most modern medicine, as many hospital treatments rely on antibiotics as a measure to control infection’, says lead author Angelo Frei from the University of Bern in Switzerland. However, growing bacterial resistance to these drugs has become a serious problem. Recently, researchers have recognised the potential of metal-based antimicrobials – including ruthenium complexes. Compared with traditional organic carbon-based chemicals, metal compounds are 10 times more likely to be active against bacteria and are not necessarily more toxic to humans, explains Frei. ‘They represent a vast compound class that has remained largely unexplored for its use in medicine,’ he adds. Ruthenium compounds are also simple to synthesise, making them easier drug candidates to explore.