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Researchers train computers to predict the next designer drugs: Global law enforcement agencies are already using the new method

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Law enforcement agencies are in a race to identify and regulate new versions of dangerous psychoactive drugs such as bath salts and synthetic opioids, even as clandestine chemists work to synthesize and distribute new molecules with the same psychoactive effects as classical drugs of abuse. Identifying these so-called "legal highs" within seized pills or powders can take months, during which time thousands of people may have already used a new designer drug. But new research is already helping law enforcement agencies around the world to cut identification time down from months to days, crucial in the race to identify and regulate new versions of dangerous psychoactive drugs. "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 UBC medical student Dr. Michael Skinnider, who completed the research as a doctoral student at UBC's Michael Smith Laboratories.


How AI Could Prevent the Development of New Illicit Drugs

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In recent years, underground chemists have increasingly made small chemical tweaks on known recreational drugs to skirt laws, creating novel designer versions. Instead of cannabis, for instance, these chemists could offer up XLR-11, or instead of PCP they might have 3-MeO-PCE. Novel designer drugs, also called research chemicals or legal highs, still produce physiological and psychological effects, though experts say that they can come with a slew of risks. Synthetic opioids such as fentanyl, for instance, are increasingly cited among the number of opioid-related deaths in the United States, which reached more than 75,000 this year. According to the Centers for Disease Control and Prevention, synthetic cannabinoids can cause heart attacks, kidney failure, and, in some cases, death.


How AI Could Prevent the Development of New Illicit Drugs

#artificialintelligence

IN RECENT YEARS, underground chemists have increasingly made small chemical tweaks on known recreational drugs to skirt laws, creating novel designer versions. Instead of cannabis, for instance, these chemists could offer up XLR-11, or instead of PCP they might have 3-MeO-PCE. Novel designer drugs, also called research chemicals or legal highs, still produce physiological and psychological effects, though experts say that they can come with a slew of risks. Synthetic opioids such as fentanyl, for instance, are increasingly cited among the number of opioid-related deaths in the United States, which reached more than 75,000 this year. According to the Centers for Disease Control and Prevention, synthetic cannabinoids can cause heart attacks, kidney failure, and, in some cases, death.


AI can quickly identify structure of drugs designed for legal highs

New Scientist

An AI tool can quickly suggest possible candidates for the chemical structures of psychoactive "designer drugs" from a simple analysis. The tool could fast-track the development of lab tests which screen the use of drugs that have similar effects to substances such as cocaine and heroin, but have been designed to evade detection. "Our method could cut down the time required to identify a new designer drug from weeks or months to just hours," says Michael Skinnider at the University of British Columbia in Vancouver. Skinnider and his colleagues created a machine learning tool called DarkNPS by training it with chemical structures of around 1700 known designer drugs, collected from forensic labs around the world. The training set included tandem mass spectrometry results for each drug, which is a common technique that provides information on the mass of a molecule and the elements it contains.


La veille de la cybersécurité

#artificialintelligence

An AI tool can quickly suggest possible candidates for the chemical structures of psychoactive "designer drugs" from a simple analysis. "Our method could cut down the time required to identify a new designer drug from weeks or months to just hours," says Michael Skinnider at the University of British Columbia in Canada. Skinnider and his colleagues created a machine learning tool called DarkNPS by training it with chemical structures of around 1700 known designer drugs, collected from forensic labs around the world. The training set included tandem mass spectrometry results for each drug, which is a common technique that provides information on the mass of a molecule and the elements it contains. This allowed the AI to identify patterns between tandem mass spectrometry data and chemical structures.