InterPred

InterPred is a platform to predict such interference chemicals based on the first large-scale chemical screening effort to directly characterize chemical-assay interference, using assays in the Tox21 portfolio specifically designed to measure autofluorescence and luciferase inhibition. InterPred combines 17 quantitative structure activity relationship (QSAR) models built using optimized machine learning techniques and allows users to predict the probability that a new chemical will interfere with different combinations of cellular and technology conditions. InterPred models have been applied to the entire Distributed Structure-Searchable Toxicity (DSSTox) Database (∼800,000 chemicals).InterPred是一款用于预测此类干扰化学品的平台，其基于首个旨在直接表征化学物质与检测分析之间干扰特性的大规模化学筛选项目，专门采用Tox21项目组合中为测量自发荧光和荧光素酶抑制而设计的检测方法。InterPred整合了17个基于优化机器学习技术构建的定量构效关系（quantitative structure activity relationship, QSAR）模型，支持用户预测新化学物质对不同细胞与实验技术条件组合产生干扰的概率。目前，InterPred模型已被应用于全量分布式结构-可搜索毒性（Distributed Structure-Searchable Toxicity, DSSTox）数据库，该数据库涵盖约80万种化学物质。