‘AI-scientist' discovers that common medication could kill cancer cells
Commonly used non-cancer drugs could help in the treatment of the disease, an 'AI-scientist' has discovered.
It seems that technology is reaching new heights, as an AI-powered 'scientist' has made a significant discovery. Working alongside human researchers, the AI model GPT-4 (not to be confused with ChatGPT) has suggested that combinations of cheap and safe drugs could also be effective at treating cancer.
The research team, led by the University of Cambridge, used the GPT-4 large language model (LLM) to sift through extensive heaps of scientific literature in order to identify potential new cancer drugs. It was found that drugs for conditions such as high cholesterol and alcohol dependence could potentially kill cancer cells, in research results published in the Journal of the Royal Society Interface.
The researchers asked GPT-4 to identify potential new drug combinations that could have an impact on a type of breast cancer cell which is commonly used in medical research. They instructed the 'AI scientist' to avoid standard cancer drugs and identify medications that would attack cancer cells without harming healthy cells.
They also prompted the AI model to prioritise drugs that were affordable and approved by regulators.
When GPT-4 had made its suggestions, the chosen drugs were then tested by human scientists to measure their effectiveness against breast cancer cells.
It was found that three of the 12 drug combinations suggested by GPT-4 worked better than current breast cancer drugs. The AI model then learned from these tests and suggested a further four combinations, three of which also showed promising results.
Simvastatin (commonly used to lower cholesterol) and disulfiram (used in alcohol dependence) stood out against breast cancer cells. And while these drugs are not traditionally associated with cancer care, they could be used as potential cancer treatments- although they would first have to go through extensive clinical trials.
The researchers have emphasised that AI is not a replacement for scientists, but that supervised AI researchers have the potential to accelerate discovery in areas like cancer research.
Models like GPT-4 have been known to return results that aren't true.
But in scientific research, these incorrect suggestions, which are known as hallucinations, can still lead to new ideas that are worth testing.
'Supervised LLMs offer a scalable, imaginative layer of scientific exploration, and can help us as human scientists explore new paths that we hadn't thought of before,' said Professor Ross King from Cambridge's Department of Chemical Engineering and Biotechnology, who led the research.
'This is not automation replacing scientists, but a new kind of collaboration,' added co-author Dr Hector Zenil from King's College London.
'Guided by expert prompts and experimental feedback, the AI functioned like a tireless research partner—rapidly navigating an immense hypothesis space and proposing ideas that would take humans alone far longer to reach.
'This study demonstrates how AI can be woven directly into the iterative loop of scientific discovery, enabling adaptive, data-informed hypothesis generation and validation in real time."
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