Screening of 6000 compounds for uncoupling activity: input parameters, the predicted uncoupling activity, as well as the results in respect to structural alerts

Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in-vitro uncoupling activity, using a biophysical model based on ab-initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural a...

在评估化学品毒性的过程中，质子载体介导的磷酸化解偶联是一项关键考量指标，近年来其在癌症、糖尿病、肥胖等疾病的治疗相关药物研发领域愈发受到关注。因此，可靠识别解偶联活性具有重要的研究价值。为此，我们以分子结构作为唯一外部输入，采用基于从头算真实溶剂类导体屏蔽模型（COSMO-RS）输入参数的生物物理模型，对6000余种阴离子化合物的体外解偶联活性开展了筛选。随后，我们将上述筛选结果与基线毒性（麻醉作用）模型进行了整合。本模型在筛选数据集中共识别出1250余种潜在解偶联剂，并发现了硫代磷酸类等新型潜在解偶联剂类别。在针对数据集中423种已知解偶联剂与612种已知无活性化合物进行验证时，该模型的灵敏度达83%，特异性达96%。在直接对比中，其特异性与结构型[原文截断]相近。