WASHINGTON, Aug. 20, 2025 — How do you identify something no one has a test for? Designer drugs replicate the effects of known, illicit drugs but evade law enforcement. The chemical structure variations that help these compounds avoid detection also make them unpredictable in the body — a quality that poses serious health consequences. Now, a research team has used computer modeling to create a database of predicted chemical structures for improved detection of designer drugs.
Jason Liang, a rising senior in the Science, Mathematics and Computer Science Magnet Program at Montgomery Blair High School, will present the team's results at the fall meeting of the American Chemical Society (ACS). ACS Fall 2025 is being held Aug. 17-21; it features about 9,000 presentations on a range of science topics.
"This library of computationally generated metabolic signatures and mass spectra, which we're calling the Drugs of Abuse Metabolite Database [DAMD], could lead to more thorough detection of new psychoactive substances and more accurate surveillance of designer drug usage," says Liang.
An illicit drug that can be misused is usually identified by its chemical "fingerprint" called a mass spectrum. This fingerprint is a pattern created by the shape, weight and makeup of the drug molecule. When a person's urine is screened for drugs, a technician uses a technique called mass spectrometry to acquire and compare spectra from molecules in the sample to catalogues of spectra for known drugs and their metabolites (small molecules created when the body breaks down a drug). However, new psychoactive substances and their metabolites don't usually have matches in existing databases.
"It's a bit of a chicken and the egg problem," says Liang's mentor Tytus Mak, a statistician and data scientist with the mass spectrometry center at the National Institute of Standards and Technology (NIST). "How do you know what this new drug is if you've never measured it, and how do you measure it if you don't know what you're looking for? Using a computational prediction methodology could help us find a solution."
The idea to develop DAMD started with Mak and Hani Habra, a former postdoc at NIST and a current bioinformatician at Michigan State University. They thought that computer modeling could keep up with the seemingly endless iterations of unknown synthetic compounds burdening health care systems and challenging drug surveillance initiatives. Then, in the summer of 2024, Mak and Habra approached Liang about working with them.
"Building a predicted mass-spectral library requires strong programming skills and a solid foundation in chemistry — both of which align well with my background," says Liang. "After learning about the devastating number of overdose deaths, including cases within the local community, I was eager to work on this project that could potentially help people."
As a starting point, the researchers used the mass-spectral database maintained by the U.S. Drug Enforcement Administration-chaired Scientific Working Group for the Analysis of Seized Drugs (SWGDRUG). This database provides reliable mass spectra for the identification of more than 2,000 drugs confiscated by law enforcement. Then, using computational approaches, Habra, Liang and Mak predicted nearly 20,000 chemical structures and corresponding mass-spectral fingerprints for possible metabolites of SWGDRUG substances and their metabolites.
The team is currently validating their predicted mass spectra by matching them to real spectra from datasets of human urine analyses. These datasets are catalogs of spectra from all detectable substances found in human urine samples. Finding a match, or something close to a match, in these datasets "tells us if the chemical structures and spectra our algorithms are producing are plausible," says Habra. Subsequently, the team will compare already-collected, real-world data to DAMD, showing a proof-of concept for forensic toxicology.
DAMD could someday be a publicly available supplement to the current illicit drug mass-spectral databases, making it easier to detect and identify evidence of drug use in human urine samples. One of its primary applications is to develop a system to help people get the medical interventions they need.
"Someone could have ingested a substance that, unbeknownst to them, was cut with a fentanyl derivative," says Mak. "Using DAMD, a doctor could see metabolites from the person's toxicological report that are strong candidates for a fentanyl-like drug and inform their treatment plan."
The research was funded by the National Institute of Standards and Technology.
Visit the ACS Fall 2025 program to learn more about this presentation, "Building the drugs of abuse metabolite database (DAMD)" and other science presentations.
