In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

Author(s)
Jennifer Hemmerich, Gerhard F. Ecker
Abstract

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

Organisation(s)
Journal
Wiley Interdisciplinary Reviews. Computational Molecular Science
Volume
10
No. of pages
23
DOI
https://doi.org/10.1002/wcms.1475
Publication date
07-2020
Peer reviewed
Yes
Austrian Fields of Science 2012
102019 Machine learning, 301211 Toxicology
Keywords
ASJC Scopus subject areas
Computational Mathematics, Materials Chemistry, Biochemistry, Computer Science Applications, Physical and Theoretical Chemistry
Portal url
https://ucris.univie.ac.at/portal/en/publications/in-silico-toxicology-from-structureactivity-relationships-towards-deep-learning-and-adverse-outcome-pathways(a4970443-90d5-462a-94d6-6684cabdde5e).html