Scientists have, for the first time, discovered a new type of antibiotic using artificial intelligence (AI). The discovery has been heralded as a major breakthrough in the fight against the growing problem of drug resistance.
According to the research powerful algorithm was used to analyse more than one hundred million chemical compounds in a matter of days.
The newly discovered compound was able to kill 35 types of potentially deadly bacteria, said researchers.
Reports suggest that antibiotic-resistant infections have risen in recent years - up 9% in England between 2017 and 2018, to nearly 61,000.
If antibiotics are taken inappropriately, harmful bacteria living inside the body can become resistant to them, which means the medicines may not work when really needed.
The World Health Organization (WHO) has called the phenomenon "one of the biggest threats to global health security and development today".
The discovery was made using an algorithm inspired by the architecture of the human brain.
Scientists trained it to analyse the structure of 2,500 drugs and other compounds to find those with the most anti-bacterial qualities that could kill E. coli.
Since 2014, the UK has cut the number of antibiotics it uses by more than 7%, but the number of drug-resistant bloodstream infections increased by 35% from 2013 to 2017.
Researchers added that the use of the machine to accelerate drug discovery could help bring down the cost of generating more antibiotics in future.
It comes just weeks after a drug molecule discovered by AI was set to become the first of its kind to be used in human trials. It will be used to treat patients who have obsessive compulsive disorder (OCD).
The use of AI technology within healthcare remains in its infancy, but major breakthroughs continue to be made.
Another recent study has claimed that AI is more accurate than doctors in diagnosing breast cancer from mammograms.
An international team, including researchers from Google Health and Imperial College London, designed and trained a computer model on X-ray images from nearly 29,000 women.
The algorithm outperformed six radiologists in reading mammograms.
