Density functional theory in the prediction of mutagenicity: A perspective

Townsend, Piers A.; Grayson, Matthew N.

Abstract

As a field, computational toxicology is concerned with using in silico 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.

• Journal Article Type: Article
• Acceptance Date: Jul 1, 2020
• Online Publication Date: Jul 9, 2020
• Publication Date: Feb 15, 2021
• Deposit Date: Sep 5, 2022
• Publicly Available Date: Sep 6, 2022
• Journal: Chemical Research in Toxicology
• Print ISSN: 0893-228X
• Electronic ISSN: 1520-5010
• Publisher: American Chemical Society
• Peer Reviewed: Peer Reviewed
• Volume: 34
• Issue: 2
• Pages: 179-188
• Series Title: This article is part of the Computational Toxicology special issue.
• DOI: https://doi.org/10.1021/acs.chemrestox.0c00113
• Keywords: Toxicology; DFT; Computational Chemistry; Reaction Modelling
• Public URL: https://uwe-repository.worktribe.com/output/9949031
• Publisher URL: https://pubs.acs.org/doi/10.1021/acs.chemrestox.0c00113