TY - JOUR
T1 - Density Functional Theory in the Prediction of Mutagenicity: A Perspective
AU - Townsend, Piers
AU - Grayson, Matthew N.
N1 - Funding Information:
This work was supported by the Engineering and Physical Sciences Research Council (EP/L016354/1) and the University of Bath.
Publisher Copyright:
© 2021 American Chemical Society. All rights reserved.
PY - 2021/2/15
Y1 - 2021/2/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85102153234&partnerID=8YFLogxK
U2 - 10.1021/acs.chemrestox.0c00113
DO - 10.1021/acs.chemrestox.0c00113
M3 - Article
VL - 34
SP - 179
EP - 188
JO - Chemical Research in Toxicology
JF - Chemical Research in Toxicology
SN - 0893-228X
IS - 2
ER -