Floresta Giuseppe, Abbate Vincenzo
Department of Analytical, Environmental and Forensic Sciences, King's College London London UK
RSC Adv. 2021 Apr 20;11(24):14587-14595. doi: 10.1039/d1ra01335a. eCollection 2021 Apr 15.
Serotonergic psychedelics, substances exerting their effects primarily through the serotonin 2A receptor (5HT2AR), continue to comprise a substantial portion of reported new psychoactive substances (NPS). In this paper five quantitative structure-activity relationship (QSAR) models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models, exploiting the accessibility of the QSAR equations, generate a useful tool for the investigation and identification of unclassified molecules. The models have been built using a set of 375 molecules using Forge software, and the quality was confirmed by statistical analysis, resulting in effective tools with respect to their predictive and descriptive capabilities. The best performing algorithm among the machine learning approaches and the classical field 3D-QSAR model were then combined to produce a consensus model and were exploited, together with a pharmacophorefilter, to explore the 5-HT2AR activity of 523 105 natural products, to classify a set of recently reported 5-HT2AR NPS and to design new potential active molecules. The findings of this study should facilitate the identification and classification of emerging 5-HT2AR ligands including NPS.
血清素能致幻剂主要通过血清素2A受体（5HT2AR）发挥作用，在新出现的精神活性物质（NPS）报告中仍占很大比例。本文建立了五个用于预测5-HT2AR配体亲和力的定量构效关系（QSAR）模型。利用QSAR方程的可及性，所得模型为未分类分子的研究和鉴定提供了有用工具。这些模型使用Forge软件，基于一组375个分子构建，通过统计分析确认了其质量，从而形成了具有预测和描述能力的有效工具。然后将机器学习方法中表现最佳的算法与经典的场3D-QSAR模型相结合，生成一个共识模型，并与药效团过滤器一起用于探索523105种天然产物的5-HT2AR活性，对一组最近报告的5-HT2AR NPS进行分类，并设计新的潜在活性分子。本研究结果应有助于识别和分类包括NPS在内的新型5-HT2AR配体。
