An online platform powered by deep learning can predict the makeup of new psychoactive substances to help law enforcement in the fight against dangerous drugs.

Called NPS-MS, the platform houses a method that predicts novel psychoactive substances using deep learning, a type of machine learning in the field of artificial intelligence that involves training computing algorithms using large data sets to uncover complex relationships and create predictive models.

“Illegal drugs are a small group of very similar-looking structures,” says Fei Wang, a doctoral student in the Department of Computing Science at the University of Alberta and first author on the international study. “The nature of psychoactive substances is that their structures are constantly evolving.”

More than 1,000 such substances have been synthesized in the past decade, designed to mimic the effects of drugs like cocaine and methamphetamine while skirting laws that don’t yet account for new chemical analogs.

“We hope this program will reduce the flow of illegal drugs that hurt people and society,” says study co-author Russ Greiner, computing science professor and Canada CIFAR AI Chair at the Alberta Machine Intelligence Institute (Amii).

Laboratory work to identify novel psychoactive substances requires expensive reference data and labor-intensive testing to produce spectrographsÔÇöchemical information references that can be used to confirm an unknown substance.