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

Arwa B. Raies, Vladimir B. Bajic

Research output: Contribution to journalArticlepeer-review

167 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)147-172
Number of pages26
JournalWiley Interdisciplinary Reviews: Computational Molecular Science
Volume6
Issue number2
DOIs
StatePublished - Jan 6 2016

ASJC Scopus subject areas

  • Physical and Theoretical Chemistry
  • Biochemistry
  • Computational Mathematics
  • Computer Science Applications
  • Materials Chemistry

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