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Computational toxicology in drug safety research

Authors: Hiroshi, Yamada;

Computational toxicology in drug safety research

Abstract

The progress of computational toxicology (CompTox) in drug safety research is highly anticipated. CompTox provides toxicity screening methods for drug discovery in the early stages. CompTox also contributes to fostering the application of the principles of the 3Rs in toxicity testing by expanding non-animal test methods. The mechanism of toxicity is complex and varied, and drug discovery modalities are becoming more diverse. Consequently, the research is considered necessary to predict toxicity using not only chemical structures but experimental data as well. Additionally, various perspectives, such as interpretation of toxicity mechanisms and species differences, must be considered in risk assessment and management in drug safety research. Therefore, it is important to construct a comprehensive CompTox system that not only presents toxicity prediction results but also provides much information related to the relationship between drug candidate substances and living organisms. In this review paper, CompTox is positioned as a discipline of toxicology that applies computer-based technology, including AI (artificial intelligence). I also introduce toxicity prediction systems based on experimental data and an ontology system that supports the interpretation of toxicity prediction results as examples of research on constructing the foundation of a comprehensive CompTox system.

Keywords

Artificial Intelligence, Toxicity Tests, Drug Discovery

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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