In Silico Toxicology: From Structure-activity Relationships Towards Deep Learning and Adverse Outcome Pathways

Overview

Journal Wiley Interdiscip Rev Comput Mol Sci

Specialty Biochemistry

Date 2022 Jul 22

PMID 35866138

Authors

Jennifer Hemmerich

Gerhard F Ecker

Affiliations

Soon will be listed here.

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 BiophysicsData Science > Chemoinformatics.

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