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计算机预测化学诱变性:整合 QSAR 模型和相似预测信息的证据权重方法。

In Silico Prediction of Chemically Induced Mutagenicity: A Weight of Evidence Approach Integrating Information from QSAR Models and Read-Across Predictions.

机构信息

INERIS-Institut National de l'Environnement Industriel et des Risques, Verneuil-en-Halatte, France.

Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Laboratory of Environmental Chemistry and Toxicology, Milan, Italy.

出版信息

Methods Mol Biol. 2022;2425:149-183. doi: 10.1007/978-1-0716-1960-5_7.

Abstract

Information on genotoxicity is an essential piece of information in the framework of several regulations aimed at evaluating chemical toxicity. In this context, QSAR models that can predict Ames genotoxicity can conveniently provide relevant information. Indeed, they can be straightforwardly and rapidly used for predicting the presence or absence of genotoxic hazards associated with the interactions of chemicals with DNA. Nevertheless, and despite their ease of use, the main interpretative challenge is related to a critical assessment of the information that can be gathered, thanks to these tools. This chapter provides guidance on how to use freely available QSAR and read-across tools provided by VEGA HUB and on how to interpret their predictions according to a weight-of-evidence approach.

摘要

有关遗传毒性的信息是旨在评估化学毒性的若干法规框架中的重要信息。在这种情况下,能够预测 Ames 遗传毒性的定量构效关系模型可以方便地提供相关信息。实际上,它们可以直接快速地用于预测与化学物质与 DNA 相互作用相关的遗传毒性危害的存在或不存在。然而,尽管它们易于使用,但主要的解释性挑战与对这些工具收集到的信息进行批判性评估有关。本章提供了关于如何使用 VEGA HUB 提供的免费 QSAR 和读通工具以及如何根据证据权重方法解释其预测的指导。

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