Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project

Quantitative structure−activity relationship (QSAR) models are powerful in silico tools for predicting the mutagenicity of unstable compounds, impurities and metabolites that are difficult to examine using the Ames test. Ideally, Ames/QSAR models for regulatory use should demonstrate high sensitivity, low false-negative rate and wide coverage of chemical space. To promote superior model development, the Division of Genetics and Mutagenesis, National Institute of Health Sciences, Japan (DGM/NIHS), conducted the Second Ames/QSAR International Challenge Project (2020–2022) as a successor to the First Project (2014–2017), with 21 teams from 11 countries participating. The DGM/NIHS provided a curated training dataset of approximately 12,000 chemicals and a trial dataset of approximately 1,600 chemicals, and each participating team predicted the Ames mutagenicity of each trial chemical using various Ames/QSAR models. The DGM/NIHS then provided the Ames test results for trial chemicals to assist in model improvement. Although overall model performance on the Second Project was not superior to that on the First, models from the eight teams participating in both projects achieved higher sensitivity than models from teams participating in only the Second Project. Thus, these evaluations have facilitated the development of QSAR models.

定量构效关系（Quantitative structure−activity relationship, QSAR）模型是一类强大的虚拟实验工具，可用于预测难以通过艾姆斯试验（Ames test）开展检测的不稳定化合物、杂质及代谢物的致突变性。理想状态下，用于监管场景的艾姆斯/QSAR模型应具备高灵敏度、低假阴性率，且能覆盖广泛的化学空间。为推动优质模型的开发，日本国立卫生科学研究所遗传与致突变研究部（Division of Genetics and Mutagenesis, National Institute of Health Sciences, DGM/NIHS）承接首届项目（2014–2017），于2020–2022年开展了第二届艾姆斯/QSAR国际挑战赛项目，共有来自11个国家的21支团队参与。该研究部门提供了包含约12000种化学品的精选训练数据集，以及包含约1600种化学品的试验数据集；各参赛团队需基于各类艾姆斯/QSAR模型，对每一种试验用化学品的艾姆斯致突变性进行预测。随后，DGM/NIHS公布了试验化学品的艾姆斯试验结果，以辅助各团队优化模型。尽管第二届项目的整体模型性能未优于首届，但同时参与两届项目的8支团队所开发的模型，其灵敏度高于仅参与第二届项目的团队所开发的模型。综上，本次评估活动有力推动了QSAR模型的研发工作。