In silico toxicology: computational methods for the prediction of chemical toxicity

Abstract
Determining the toxicity of chemicals is necessary to identify their harmful effects on humans, animals, plants, or the environment. It is also one of the main steps in drug design. Animal models have been used for a long time for toxicity testing. However, in vivo animal tests are constrained by time, ethical considerations, and financial burden. Therefore, computational methods for estimating the toxicity of chemicals are considered useful. In silico toxicology is one type of toxicity assessment that uses computational methods to analyze, simulate, visualize, or predict the toxicity of chemicals. In silico toxicology aims to complement existing toxicity tests to predict toxicity, prioritize chemicals, guide toxicity tests, and minimize late-stage failures in drugs design. There are various methods for generating models to predict toxicity endpoints. We provide a comprehensive overview, explain, and compare the strengths and weaknesses of the existing modeling methods and algorithms for toxicity prediction with a particular (but not exclusive) emphasis on computational tools that can implement these methods and refer to expert systems that deploy the prediction models. Finally, we briefly review a number of new research directions in in silico toxicology and provide recommendations for designing in silico models.

Citation
In silico toxicology: computational methods for the prediction of chemical toxicity 2016:n/a Wiley Interdisciplinary Reviews: Computational Molecular Science

Publisher
Wiley

Journal
Wiley Interdisciplinary Reviews: Computational Molecular Science

DOI
10.1002/wcms.1240
10.1002/wcms.1254

PubMed ID
27066112

Additional Links
http://doi.wiley.com/10.1002/wcms.1240

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