Journal
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
Volume 10, Issue 4, Pages -Publisher
WILEY
DOI: 10.1002/wcms.1475
Keywords
adverse outcome pathway; computational toxicology; in silico toxicology; machine learning; read across
Funding
- Austrian Science Fund [W1232]
- Innovative Medicines Initiative [777365]
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In silico toxicology is an emerging field. It gains increasing importance as research is aiming to decrease the use of animal experiments as suggested in the 3R principles by Russell and Burch. In silico toxicology is a means to identify hazards of compounds before synthesis, and thus in very early stages of drug development. For chemical industries, as well as regulatory agencies it can aid in gap-filling and guide risk minimization strategies. Techniques such as structural alerts, read-across, quantitative structure-activity relationship, machine learning, and deep learning allow to use in silico toxicology in many cases, some even when data is scarce. Especially the concept of adverse outcome pathways puts all techniques into a broader context and can elucidate predictions by mechanistic insights. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Chemoinformatics
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