How can you detect a substance when no test exists for it? Designer drugs mimic the effects of established illicit drugs while slipping past law enforcement. Their modified chemical structures help them evade detection while making their effects on the body unpredictable and dangerous.
High School Researcher Presents New Database for Tracking Designer Drugs
A research group has now applied computer modeling to build a database of predicted chemical structures aimed at enhancing the detection of designer drugs.
Jason Liang, an upcoming senior in the Science, Mathematics, and Computer Science Magnet Program at Montgomery Blair High School, shared the team’s findings at the American Chemical Society’s Fall 2025 meeting, held August 17–21.
“This database of predicted metabolic signatures and spectra, called DAMD, could improve detection and monitoring of emerging designer drugs,” says Liang.
Illicit drugs are typically identified by a unique chemical “fingerprint,” known as a mass spectrum. This fingerprint reflects the molecule’s structure, weight, and composition.
Why Standard Drug Tests Miss New Psychoactive Substances
In urine drug tests, technicians use mass spectrometry to compare molecular spectra with catalogs of known drugs and their metabolites. But because new psychoactive substances and their metabolites rarely appear in current databases, they often go undetected.
“It’s a classic chicken-and-egg dilemma,” says Liang’s mentor, Tytus Mak, a statistician and data scientist at the National Institute of Standards and Technology (NIST) mass spectrometry center.
“How do you identify a drug that’s never been measured, or measure it if you don’t know what to look for?” Could computational prediction provide a way forward?”
From Concept to Collaboration: The Origins of DAMD
The idea for DAMD began with Mak and Hani Habra, a former NIST postdoc now at Michigan State University. They suggested computer modeling could track the constant influx of new synthetic compounds burdening health systems and drug monitoring. In summer 2024, Mak and Habra invited Liang to join the project.
“Creating a predicted mass-spectral library demands both advanced programming abilities and a strong grasp of chemistry—skills that match my background well,” says Liang.
“After seeing the devastating toll of overdose deaths, including cases in my own community, I was motivated to contribute to a project that might make a difference.”
The team began with the mass-spectral database curated by SWGDRUG, chaired by the U.S. Drug Enforcement Administration. This resource contains validated mass spectra for identifying over 2,000 substances seized by law enforcement.
Using computational methods, Habra, Liang, and Mak generated nearly 20,000 predicted chemical structures along with their mass-spectral fingerprints for potential metabolites of SWGDRUG-listed substances and their derivatives.
Validating Predictions Against Real-World Urine Data
The researchers are now validating these predictions by comparing them with actual spectra from human urine analysis datasets—comprehensive catalogs of all detectable compounds present in urine samples.
“If we find a match, or even something close, it indicates that the chemical structures and spectra produced by our algorithms are realistic,” explains Habra. The next step is to test DAMD against existing real-world data, providing a proof of concept for forensic toxicology.
In the future, DAMD could expand public drug databases to improve detection and identification in urine samples. A key goal is to support timely medical intervention.
“For example, someone might unknowingly ingest a substance laced with a fentanyl derivative,” Mak says. “With DAMD, doctors could identify fentanyl-like metabolites in a toxicology report and adjust treatment accordingly.”
Read the original article on: Phys.Org
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