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是一款干扰化学品预测平台，其依托首个旨在直接表征化学物质与检测方法干扰特性的大规模化学筛选工作构建，该工作采用了Tox21项目组合中专门用于检测自发荧光与荧光素酶抑制作用的检测实验。InterPred整合了17个经优化机器学习技术构建的定量构效关系（quantitative structure activity relationship，QSAR）模型，可支持用户预测新型化学物质对不同细胞与实验技术组合产生干扰的概率。目前，InterPred模型已被应用于完整的分布式结构可检索毒性（Distributed Structure-Searchable Toxicity，DSSTox）数据库，该数据库涵盖约80万种化学物质。

搜集汇总

背景与挑战

背景概述

InterPred是一个基于大规模化学筛选的预测平台，专注于预测化学物质在生物检测中的干扰行为。它结合了17个优化的机器学习QSAR模型，允许用户评估新化学物质在不同实验条件下的干扰概率，并已应用于包含约80万种化学物质的DSSTox数据库，支持毒理学研究中的化学干扰风险评估。