Toxicological Modelling

Moss, Gary; Wilkinson, Simon

Abstract

Computational (or in silico) methods aim to complement the standard cascade of in vitro to in vivo toxicity testing by using algorithms, statistical methods or, more recently, artificial intelligence–based methods to increase the speed of the process and to minimise the need for animal testing, where appropriate. A wide range of computational methods are employed to computationally estimate toxic endpoints. These include databases of chemical information, algorithm-based methods such as quantitative structure–activity relationships, molecular/structural descriptors and Machine Learning methods. The field of in silico toxicology is enormous, and as such this chapter focuses on the key underlying aspects of toxicological modelling and in particular the use of algorithms and related methods used to provide estimates of toxicity, as well as the selection of appropriate methods to predict toxicity.

Citation

Moss, G., & Wilkinson, S. (2021). Toxicological Modelling. In Toxicology for the Health and Pharmaceutical Sciences. (1). Taylor and Francis Group. https://doi.org/10.1201/9780203730584

• Online Publication Date: Dec 21, 2021
• Publication Date: 2021
• Deposit Date: Jan 7, 2025
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Edition: 1
• Book Title: Toxicology for the Health and Pharmaceutical Sciences
• Chapter Number: 17
• DOI: https://doi.org/10.1201/9780203730584
• Public URL: https://keele-repository.worktribe.com/output/1045108
• Publisher URL: https://www.taylorfrancis.com/chapters/edit/10.1201/9780203730584-17/toxicological-modelling-gary-moss-simon-wilkinson