Chemoinformatic Consideration of Novel Psychoactive Substances: Compilation and Preliminary Analysis of a Categorised Dataset
James W. Firman, Samuel J. Belfield, George Chen, Megan N. Jackson, Fai Hou Lam, Callum Richmond, James Smith, Fabian P. Steinmetz, M Cronin
Molecular Informatics January 17, 2019 DOI: 10.1002/minf.201800142 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Systematic compilation and classification Peer reviewed |
|---|---|
| Keywords | Scope computer science Cheminformatics Drugs of abuse Action physics Drug discovery Data science Computational biology Pharmacology Bioinformatics Computational chemistry |
| Key points | A dataset of 690 NPS and 155 EPDA was classified into over forty chemical groupings, seven subjective effect categories, and six pharmacological mechanisms, with common co-occurrence indicating scope for chemical read-across and structural alerts. |
Abstract
Recent years have seen the emergence into circulation of a growing array of novel psychoactive substances (NPS). Knowledge of the pharmacological profiles and risk liability of these compounds is typically very scarce. Development of chemoinformatic tools enabling prediction of properties within uncharacterised analogues has potential be of particular use. In order to facilitate this, compilation of a chemical inventory comprising known NPS is a necessity. Sourcing a variety of published governmental and analytical reports, a dataset composed of 690 distinct acknowledged NPS, complete with defined chemical structures, has been constructed. This is supplemented by a complementary series of 155 established psychoactive drugs of abuse (EPDA). Classification was performed in accordance with their key molecular structural features, subjective effect profiles and pharmacological mechanisms of action. In excess of forty chemical groupings, spanning seven subjective effect categories and six broad mechanisms of pharmacological action, were identified. Co-occurrence of NPS and EPDA within specific classes was common, showcasing inherent scope both for chemical read-across and for the derivation of structural alerts.