Computational approaches to chemical hazard assessment

Thomas Luechtefeld, Thomas Hartung

Research output: Contribution to journalArticlepeer-review

Abstract

Computational prediction of toxicity has reached new heights as a result of decades of growth in the magnitude and diversity of biological data. Public packages for statistics and machine learning make model creation faster. New theory in machine learning and cheminformatics enables integration of chemical structure, toxicogenomics, simulated and physical data in the prediction of chemical health hazards, and other toxicological information. Our earlier publications have characterized a toxicological dataset of unprecedented scale resulting from the European REACH legislation (Registration Evaluation Authorisation and Restriction of Chemicals). These publications dove into potential use cases for regulatory data and some models for exploiting this data. This article analyzes the options for the identification and categorization of chemicals, moves on to the derivation of descriptive features for chemicals, discusses different kinds of targets modeled in computational toxicology, and ends with a high-level perspective of the algorithms used to create computational toxicology models.

Original languageEnglish (US)
Pages (from-to)459-478
Number of pages20
JournalALTEX
Volume34
Issue number4
DOIs
StatePublished - 2017

Keywords

  • Cheminformatics
  • Machine Learning
  • Molecular descriptor
  • QSAR
  • Toxicology

ASJC Scopus subject areas

  • Pharmacology
  • Medical Laboratory Technology

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