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

Molecules

Dachuan Zhang¹  Rui Zhang¹  Mengying Han¹  Linlin Gong¹  Huadong Xing¹  Dongliang Liu¹  Pengli Cai¹  Qian-Nan Hu¹  Sheng Liu¹  Dandan Sun¹  Junni Chen²  Weizhong Tu² 

[1] CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;Wuhan LifeSynther Science and Technology Co., Limited, Wuhan 430000, China;

关键词: drug addiction;    database;    new psychoactive substance;    machine learning;    prediction;   

DOI  :  10.3390/molecules27123931

来源: DOAJ

【 摘 要 】

The mechanisms underlying drug addiction remain nebulous. Furthermore, new psychoactive substances (NPS) are being developed to circumvent legal control; hence, rapid NPS identification is urgently needed. Here, we present the construction of the comprehensive database of controlled substances, AddictedChem. This database integrates the following information on controlled substances from the US Drug Enforcement Administration: physical and chemical characteristics; classified literature by Medical Subject Headings terms and target binding data; absorption, distribution, metabolism, excretion, and toxicity; and related genes, pathways, and bioassays. We created 29 predictive models for NPS identification using five machine learning algorithms and seven molecular descriptors. The best performing models achieved a balanced accuracy (BA) of 0.940 with an area under the curve (AUC) of 0.986 for the test set and a BA of 0.919 and an AUC of 0.968 for the external validation set, which were subsequently used to identify potential NPS with a consensus strategy. Concurrently, a chemical space that included the properties of vectorised addictive compounds was constructed and integrated with AddictedChem, illustrating the principle of diversely existing NPS from a macro perspective. Based on these potential applications, AddictedChem could be considered a highly promising tool for NPS identification and evaluation.

【 授权许可】

Unknown