Facing the continuous emergence of new psychoactive substances (NPS) and their threat to public health, more effective methods for NPS prediction and identification are critical. In this study, the pharmacological affinity fingerprints (Ph-fp) of NPS compounds were predicted by Random Forest classification models using bioactivity data from the ChEMBL database. The binary Ph-fp is the vector consisting of a compound’s activity against a list of molecular targets reported to be responsible for the pharmacological effects of NPS. Their performance in similarity searching and unsupervised clustering was assessed and compared to 2D structure fingerprints Morgan and MACCS (1024-bits ECFP4 and 166-bits SMARTS-based MACCS implementation of RDKit). The performance in retrieving compounds according to their pharmacological categorizations is influenced by the predicted active assay counts in Ph-fp and the choice of similarity metric. Overall, the comparative unsupervised clustering analysis suggests the use of a classification model with Morgan fingerprints as input for the construction of Ph-fp. This combination gives satisfactory clustering performance based on external and internal clustering validation indices.

中文翻译：

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源自生物活性数据的药理学亲和指纹用于识别设计药物

面对新的精神活性物质 (NPS) 的不断出现及其对公众健康的威胁，更有效的 NPS 预测和识别方法至关重要。在本研究中，使用来自 ChEMBL 数据库的生物活性数据，通过随机森林分类模型预测 NPS 化合物的药理亲和指纹 (Ph-fp)。二元 Ph-fp 是由化合物对一系列分子靶点的活性组成的载体，据报道这些分子靶点负责 NPS 的药理作用。评估了它们在相似性搜索和无监督聚类方面的性能，并与 2D 结构指纹 Morgan 和 MACCS（RDKit 的 1024 位 ECFP4 和 166 位基于 SMARTS 的 MACCS 实现）进行了比较。根据药理学分类检索化合物的性能受 Ph-fp 中预测的活性测定计数和相似性度量的选择的影响。总体而言，比较无监督聚类分析表明使用具有摩根指纹的分类模型作为构建 Ph-fp 的输入。这种组合基于外部和内部聚类验证指标提供了令人满意的聚类性能。