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.
Flavor molecules
are commonly used in the food industry
to enhance
product quality and consumer experiences but are associated with potential
human health risks, highlighting the need for safer alternatives.
To address these health-associated challenges and promote reasonable
application, several databases for flavor molecules have been constructed.
However, no existing studies have comprehensively summarized these
data resources according to quality, focused fields, and potential
gaps. Here, we systematically summarized 25 flavor molecule databases
published within the last 20 years and revealed that data inaccessibility,
untimely updates, and nonstandard flavor descriptions are the main
limitations of current studies. We examined the development of computational
approaches (e.g., machine learning and molecular simulation) for the
identification of novel flavor molecules and discussed their major
challenges regarding throughput, model interpretability, and the lack
of gold-standard data sets for equitable model evaluation. Additionally,
we discussed future strategies for the mining and designing of novel
flavor molecules based on multi-omics and artificial intelligence
to provide a new foundation for flavor science research.
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