Titel
In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways
Autor*in
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.
Stichwort
adverse outcome pathwaycomputational toxicologyin silico toxicologymachine learningread across
Objekt-Typ
Sprache
Englisch [eng]
Persistent identifier
phaidra.univie.ac.at/o:1218504
Erschienen in
Titel
WIREs Computational Molecular Science
Band
10
Ausgabe
4
ISSN
1759-0876
Erscheinungsdatum
2020
Publication
Wiley
Fördergeber
Austrian Science Fund (FWF) — W1232
Erscheinungsdatum
2020
Zugänglichkeit
Rechte
Rechteangabe
© 2020 The Authors
Universität Wien | Universitätsring 1 | 1010 Wien | T
+43-1-4277-0