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AddictedChem: A Data-Driven Integrated Platform for New Psychoactive Substance Identification.

Mengying Han1, Sheng Liu1, Dachuan Zhang1

  • 1CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.

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Summary
This summary is machine-generated.

AddictedChem, a new database, aids in identifying novel psychoactive substances (NPS) using predictive models. This tool helps combat drug addiction by rapidly detecting emerging substances.

Keywords:
databasedrug addictionmachine learningnew psychoactive substanceprediction

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Area of Science:

  • Pharmacology and Toxicology
  • Computational Chemistry
  • Data Science

Background:

  • Drug addiction mechanisms are poorly understood.
  • New psychoactive substances (NPS) emerge rapidly, necessitating swift identification.
  • Existing databases lack comprehensive, integrated data for NPS analysis.

Purpose of the Study:

  • To develop AddictedChem, a comprehensive database for controlled substances.
  • To create predictive models for rapid NPS identification.
  • To integrate chemical space analysis for a macro-level view of NPS.

Main Methods:

  • Integrated data from DEA on controlled substances: chemical properties, literature, binding data, ADMET, genes, pathways, and bioassays.
  • Developed 29 predictive models using machine learning algorithms and molecular descriptors.
  • Constructed a chemical space of vectorized addictive compounds.

Main Results:

  • The best predictive models achieved high performance: Balanced Accuracy (BA) of 0.940 and Area Under the Curve (AUC) of 0.986 on the test set.
  • External validation demonstrated strong results: BA of 0.919 and AUC of 0.968.
  • AddictedChem integrates diverse data, enabling a macro-level understanding of NPS diversity.

Conclusions:

  • AddictedChem is a valuable resource for identifying and evaluating novel psychoactive substances.
  • The predictive models offer a robust approach to NPS detection.
  • Integrated data and chemical space analysis provide a powerful tool for drug addiction research.