Artificial intelligence robots are turbo-charging the race to find new drugs for the crippling nerve disorder ALS, or motor neurone disease.
The condition, also known as Lou Gehrig's disease, attacks and kills nerve cells controlling muscles, leading to weakness, paralysis and, ultimately, respiratory failure.
There are only two drugs approved by the U.S. Food and Drug Administration to slow the progression of ALS (amyotrophic lateral sclerosis), one available since 1995 and the other approved just this year. About 140,000 new cases are diagnosed a year globally and there is no cure for the disease, famously suffered by cosmologist Stephen Hawking.
"Many doctors call it the worst disease in medicine and the unmet need is huge," said Richard Mead of the Sheffield Institute of Translational Neuroscience, who has found artificial intelligence (AI) is already speeding up his work.
Such robots -- complex software run through powerful computers -- work as tireless and unbiased super-researchers. They analyze huge chemical, biological and medical databases, alongside reams of scientific papers, far quicker than humanly possible, throwing up new biological targets and potential drugs.
One candidate proposed by AI machines recently produced promising results in preventing the death of motor neurone cells and delaying disease onset in preclinical tests in Sheffield.
Mead, who aims to present the work at a medical meeting in December, is now assessing plans for clinical trials.
He and his team in northern England are not the only ones waking up to the ability of AI to elucidate the complexities of ALS.
In Arizona, the Barrow Neurological Institute last December found five new genes linked to ALS by using IBM's Watson supercomputer. Without the machine, researchers estimate the discovery would have taken years rather than only a few months.
Mead believes ALS is ripe for AI and machine-learning because of the rapid expansion in genetic information about the condition and the fact there are good test-tube and animal models to evaluate drug candidates.