Researchers at Vancouver's University of British Columbia (UBC) and other universities have trained a computer to predict the molecular structure of new drugs.
"There is an entire world of chemical ‘dark matter’ just beyond our fingertips right now. I think there is a huge opportunity for the right AI tools to shine a light on this unknown chemical world,” says Dr. Michael Skinnider in a press release. He completed the research as a doctoral student at UBC.
Designer drugs are newly designed drugs that skirt the law; they typically are very similar to previously developed drugs and have similar effects, but are different enough to not technically fall under the laws regulating the drug they were made to mimic, like new versions of bath salts and synthetic opioids.
Using data collected from around the world on illicit drugs, researchers trained the computer to come up with new drugs that hadn't been created yet, but that would fit the parameters. It came up with 8.9 million different chemical designs.
Then they compared 196 newly created designer drugs, that didn't exist when the computer was initially programmed, with those it had come up with.
The computer, a deep neural network, had come up with more than 175 of the drugs.
“The fact that we can predict what designer drugs are likely to emerge on the market before they actually appear is a bit like the 2002 sci-fi movie, Minority Report, where foreknowledge about criminal activities about to take place helped significantly reduce crime in a future world,” explains the University of Alberta's Dr. David Wishart, who was the senior author on the research paper.
Now that the computer can predict what chemicals are likely to appear in the near future, law agencies and public health officials can get a head start on things. Previously it would take months to identify a new designer drug after it had been found by authorities. Now it takes days.
“The vast majority of these designer drugs have never been tested in humans and are completely unregulated. They are a major public health concern to emergency departments across the world,” says Dr. Skinnider.
The model did more than just identify the structures; it also was able to predict which were more likely to appear in the marketplace.
While the technology was used to identify drugs this time, it could be used in other research on molecular structures.