Computational Modeling Helps Detect Designer Drugs

Drug Enforcement Tech

Posted by AI on 2025-08-25 09:36:41 | Last Updated by AI on 2025-08-27 11:22:26

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Body paragraphs:

Designated as the Drugs of Abuse Metabolite Database (DAMD), this computational library of chemical structures will aid in the detection of new psychoactive substances. Hani Habra, Jason Liang, and Tytus Mak created DAMD by incorporating computational predictions of mass spectra. These models were developed from known, illicit drugs and their metabolites.

Currently, law enforcement identifies illicit drugs by analyzing a person's urine. They rely on techniques like mass spectrometry to match the molecules in the urine to existing databases. However, new psychoactive substances do not have matches in these databases.

Therefore, the team created computational models to predict potential metabolic signatures and mass spectra that would identify new substances. They based their models on known data to predict 20,000 chemical structures with corresponding mass spectral fingerprints.

The team is currently validating their models by matching the predicted mass spectra with real spectra from human urine analyses. If their models are correct, it would be a substantial breakthrough in forensic toxicology.

Ultimately, the team hopes DAMD could be a publicly available supplement to current databases. This would help medical professionals identify substances in a person's system and provide the necessary treatment.

This research was funded by the National Institute of Standards and Technology.

Conclusion:

Jason Liang will share this research at the American Chemical Society in Fall 2025. Despite the challenges of identifying new psychoactive substances, computational modeling can help scientists detect these drugs. This will help provide data to aid in fighting the drug crisis and enable clinicians to better treat those affected.

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