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October 9, 202592 citations

Applications of AI in Computational Toxicology for Drug Discovery

Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.

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This review highlights how AI advancements boost ADMET and toxicity prediction accuracy in drug candidates.

The transition to computational toxicology is driven by the need for efficient toxicity risk assessment, enhancing drug discovery processes.
Over 20 prediction platforms were evaluated, including various methodologies like machine learning and statistical methods to define their usability.
Recent advancements in AI and ML are shifting focus from single-endpoint predictions to sophisticated multi-endpoint joint modeling approaches.
Challenges remain, including data quality and interpretability, but future prospects suggest improved support through interpretable models and multi-omics integration.

Zhang et al. (2025) studied this question.

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October 9, 202592 citations

Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.

View Full Paper

Ask AI

Bookmark

Authors

This review highlights how AI advancements boost ADMET and toxicity prediction accuracy in drug candidates.

The transition to computational toxicology is driven by the need for efficient toxicity risk assessment, enhancing drug discovery processes.
Over 20 prediction platforms were evaluated, including various methodologies like machine learning and statistical methods to define their usability.
Recent advancements in AI and ML are shifting focus from single-endpoint predictions to sophisticated multi-endpoint joint modeling approaches.
Challenges remain, including data quality and interpretability, but future prospects suggest improved support through interpretable models and multi-omics integration.

Zhang et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark