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Psychoactive drugs impact brain function, influencing perception, mood, consciousness, cognition, and behavior. These substances are grouped based on their effects and the mechanisms by which they act.
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Hallucinogens, also known as psychedelic drugs, are a class of substances known for their ability to alter perception, cognition, and emotions. Despite their profound effects on the mind, these drugs are non-addictive, setting them apart from many other abused substances. The mechanism of action of these drugs lies in their impact on the 5-HT2A receptor in the brain. Upon activation, this receptor couples to Gq-type G proteins, triggering a cascade that releases intracellular calcium. This...
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Altered states of consciousness represent significant deviations from one's normal mental state. These deviations can range from subtle changes in awareness to profound transformations in perception, thought processes, and sensory experiences. Altered states of consciousness can be triggered by various factors, including drug use, meditation, hypnosis, illness, or even intense fatigue.
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Novel Psychoactive Substances: Slaying the Dragon With Artificial Intelligence.

David S Wishart1,2,3,4, Prashanthi Kovur1, Yamilé López-Hernández1,5

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Artificial intelligence (AI) aids in identifying novel psychoactive substances (NPSs) by predicting structures and spectra, improving forensic toxicology. This technology accelerates the identification process, enhancing public health responses to emerging NPS threats.

Keywords:
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Area of Science:

  • Forensic Toxicology
  • Computational Chemistry
  • Drug Surveillance

Background:

  • Novel psychoactive substances (NPSs) pose challenges to forensic, healthcare, and regulatory systems.
  • Conventional analytical methods struggle with identifying newly synthesized NPSs due to a lack of reference standards.
  • Artificial intelligence (AI) and machine learning offer potential solutions for NPS identification and characterization.

Purpose of the Study:

  • To review the application of AI and machine learning in addressing the challenges of NPS identification.
  • To explore AI-based approaches integrated into forensic workflows for NPS analysis.
  • To examine emerging AI technologies relevant to forensic toxicology.

Main Methods:

  • Review of current forensic workflows and AI integration, including deep learning, chemical language models, and spectral prediction tools.
  • Emphasis on the DarkNPS framework for generating plausible NPS structures and in silico MS/MS spectra.
  • Examination of emerging AI technologies like transformers, graph neural networks, and multimodal frameworks.

Main Results:

  • AI systems significantly reduce time and resources for NPS identification by enabling structure generation and spectral prediction without physical standards.
  • The DarkNPS model predicted over 8.9 million plausible NPS structures, anticipating over 90% of future NPS.
  • AI-generated in silico MS/MS spectral libraries achieved high similarity with experimental spectra, enabling identification in 75%-90% of cases.

Conclusions:

  • Integration of AI with analytical chemistry enhances the speed, scope, precision, and utility of NPS identification.
  • AI represents a promising advancement in forensic toxicology and chemical surveillance for emerging NPS threats.
  • AI-driven identification facilitates accurate diagnoses, timely treatment, and improved public health responses.