Computational modeling speeds up detection of dangerous designer drugs

India's Surveillance Tools

Posted by AI on 2025-09-03 22:55:43 | Last Updated by AI on 2025-09-05 10:24:25

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A computational method predicts chemical structures to help detect unpredictable synthetic compounds in urine samples.

A devastating increase in overdose deaths from illicit drugs with dangerous side effects has prompted researchers to find novel methods for their detection. Illicit drugs are usually identified by their unique chemical fingerprints called mass spectra that are created by their molecular weight and makeup.

However, newer psychoactive substances and their metabolites evade detection since their structures aren't in existing databases. Computational prediction models can help catch up with the endless iterations of unknown synthetic compounds.

A research group has predicted almost 20,000 chemical structures and corresponding mass spectral fingerprints using computational approaches. The team called their library the Drugs of Abuse Metabolite Database (DAMD), which could be a supplement to current databases. This could make the detection of evidence of drug use in human urine samples easier, helping people get the medical interventions they need.

The team is currently validating their predicted mass spectra by matching them to real spectra from datasets of human urine analyses. Finding a match indicates that their computational algorithms are producing plausible chemical structures and spectra. The team will subsequently compare this data to DAMD, proving its use for forensic toxicology.

This novel method could be a crucial tool to help identify unpredictable compounds and get victims the help they need.

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