An ideal medicine for one person may prove ineffective or harmful for someone else, and predicting who could benefit from a given drug has been difficult. Now, an international team led by neuroscientist Kirill Martemyanov, Ph.D., based at The Herbert Wertheim UF Scripps Institute for Biomedical Innovation & Technology, is training artificial intelligence to assist.
"We all think of ourselves as more or less normal, but we are not. We are all basically mutants. We have tremendous variability in our cell receptors," Martemyanov said."If doctors don't know what exact genetic alteration you have, you just have this one-size-fits-all approach to prescribing, so you have to experiment to find what works for you."
Scientists have catalogued about 800 GPCRs in humans. About half are dedicated to senses, especially smell. About 250 more receive medicines or other known molecules. Martemyanov's team had to invent a new protocol to observe and document them. They found many surprises. Some GPCRs worked as expected, but others didn't, notably those for neurotransmitters called glutamate.
Classifying GPCRs solely by their best-known activity was akin to seeing one leg of an elephant, he said. It was an oversimplification, too general to train AI, Martemyanov said. When Correia's group in Switzerland trained the algorithm to make predictions based on this more nuanced data, the researchers were excited by the results. They found it to be correct more than 80% of the time.
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