预测致突变性的QSAR模型的评估:第二届Ames/QSAR国际挑战项目的结果。

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY
A Furuhama, A Kitazawa, J Yao, C E Matos Dos Santos, J Rathman, C Yang, J V Ribeiro, K Cross, G Myatt, G Raitano, E Benfenati, N Jeliazkova, R Saiakhov, S Chakravarti, R S Foster, C Bossa, C Laura Battistelli, R Benigni, T Sawada, H Wasada, T Hashimoto, M Wu, R Barzilay, P R Daga, R D Clark, J Mestres, A Montero, E Gregori-Puigjané, P Petkov, H Ivanova, O Mekenyan, S Matthews, D Guan, J Spicer, R Lui, Y Uesawa, K Kurosaki, Y Matsuzaka, S Sasaki, M T D Cronin, S J Belfield, J W Firman, N Spînu, M Qiu, J M Keca, G Gini, T Li, W Tong, H Hong, Z Liu, Y Igarashi, H Yamada, K-I Sugiyama, M Honma
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引用次数: 0

摘要

定量构效关系(QSAR)模型是预测不稳定化合物、杂质和代谢物的致突变性的强大的硅工具,这些化合物、杂质和代谢物很难用Ames测试来检测。理想情况下,用于监管用途的Ames/QSAR模型应具有高灵敏度,低假阴性率和广泛的化学空间覆盖范围。为了促进卓越模型的开发,日本国立卫生科学研究院(DGM/NIHS)遗传与诱变部(DGM/NIHS)继第一个项目(2014-2017)之后,开展了第二个Ames/QSAR国际挑战项目(2020-2022),共有来自11个国家的21个团队参加。DGM/NIHS提供了大约12,000种化学物质的训练数据集和大约1,600种化学物质的试验数据集,每个参与团队使用各种Ames/QSAR模型预测每种试验化学物质的Ames诱变性。DGM/NIHS随后提供了试验化学品的Ames测试结果,以协助模型改进。虽然第二个项目的整体模型性能并不优于第一个项目,但参与两个项目的8个团队的模型比只参与第二个项目的团队的模型获得了更高的灵敏度。因此,这些评价促进了QSAR模型的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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