Because the drugs have already been developed and approved, redeploying them can be more straightforward than starting from scratch with new formulas.
Discovering new drugs and getting them to market can take a long time – more than 10 years, according to some estimates.
But Prof Chandran and his team believe their work means affordable, effective drugs for neurological conditions could come much sooner.
The research is not the first to explore how AI can surface potential solutions hidden in mountains of health or medical data.
Scientists at the Massachusetts Institute of Technology in Cambridge, US, have used generative AI to identify novel antibiotic compounds that might be able to treat superbugs including gonorrhoea and conditions such as Parkinson’s.
And in 2024 researchers at Harvard University developed a neural network model called TxGNN, external to surface existing drugs which could be used to treat rare conditions.
But there have been setbacks in the wider field of research.
A recent review of lecanemab and donanemab, once hailed as “breakthrough” drugs to treat Alzheimer’s, found despite slowing its progression it was not significant enough to make a meaningful difference to patients.
It looked at 17 studies, involving 20,342 volunteers, of drugs that remove amyloid – a misfolded protein present in disease – from the brain.
Its conclusion sparked a backlash from other scientists.
But Professor Chandran remains confident “we’re at the tipping point of change” in neurological research and understanding.