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.
Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin 300308, China.
Molecules. 2022 Jun 19;27(12):3931. doi: 10.3390/molecules27123931.
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.
成瘾的机制仍然模糊不清。此外，新的精神活性物质（NPS）的开发是为了规避法律控制；因此，迫切需要快速识别 NPS。在这里，我们展示了受控物质综合数据库 AddictedChem 的构建。该数据库整合了来自美国缉毒署的以下受控物质信息：物理和化学特性；按医学主题词分类的文献和目标结合数据；吸收、分布、代谢、排泄和毒性；以及相关的基因、途径和生物测定。我们使用五种机器学习算法和七种分子描述符为 NPS 识别创建了 29 个预测模型。表现最好的模型在测试集上的平衡准确率（BA）为 0.940，曲线下面积（AUC）为 0.986，在外部验证集上的 BA 为 0.919，AUC 为 0.968，随后用于使用共识策略识别潜在的 NPS。同时，构建了一个包含向量成瘾化合物特性的化学空间，并将其与 AddictedChem 集成，从宏观角度说明了 NPS 多样化存在的原理。基于这些潜在的应用，AddictedChem 可以被认为是识别和评估 NPS 的一个很有前途的工具。
