Centre for Sustainable Chemical Technologies, Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom.
Department of Chemistry, University of Bath, Claverton Down, Bath BA2 7AY, United Kingdom.
Chem Res Toxicol. 2021 Feb 15;34(2):179-188. doi: 10.1021/acs.chemrestox.0c00113. Epub 2020 Aug 7.
As a field, computational toxicology is concerned with using models to predict and understand the origins of toxicity. It is fast, relatively inexpensive, and avoids the ethical conundrum of using animals in scientific experimentation. In this perspective, we discuss the importance of computational models in toxicology, with a specific focus on the different model types that can be used in predictive toxicological approaches toward mutagenicity (SARs and QSARs). We then focus on how quantum chemical methods, such as density functional theory (DFT), have previously been used in the prediction of mutagenicity. It is then discussed how DFT allows for the development of new chemical descriptors that focus on capturing the steric and energetic effects that influence toxicological reactions. We hope to demonstrate the role that DFT plays in understanding the fundamental, intrinsic chemistry of toxicological reactions in predictive toxicology.
作为一个领域，计算毒理学关注使用模型来预测和理解毒性的起源。它快速、相对便宜，并且避免了在科学实验中使用动物的伦理难题。在这个观点中，我们讨论了计算模型在毒理学中的重要性，特别关注可用于预测毒理学方法中的不同模型类型，如致突变性（SARs 和 QSARs）。然后，我们专注于密度泛函理论 (DFT) 等量子化学方法如何以前用于预测致突变性。然后讨论了 DFT 如何允许开发新的化学描述符，这些描述符专注于捕捉影响毒理学反应的空间和能量效应。我们希望展示 DFT 在理解预测毒理学中毒理学反应的基本内在化学中的作用。
