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Artificial Intelligence-Driven Drug Toxicity Prediction: Advances, Challenges, and Future Directions.

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Artificial Intelligence (AI) is revolutionizing drug toxicity prediction, overcoming limitations of traditional methods. This review details AI

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Area of Science:

  • Computational toxicology
  • Drug discovery and development
  • Artificial Intelligence in medicine

Background:

  • Traditional drug toxicity prediction methods face challenges like high costs, low throughput, and cross-species extrapolation uncertainty.
  • These limitations hinder efficient new drug research and development.
  • Artificial Intelligence (AI) offers a transformative approach to drug toxicology assessment.

Purpose of the Study:

  • To systematically review the global literature and development status of AI applications in drug toxicity prediction.
  • To analyze the utilization of various toxicity databases and prediction methods for different toxicity endpoints.
  • To discuss the progress, advantages, challenges, and future directions of AI in drug toxicity prediction.

Main Methods:

  • Comprehensive literature review focusing on AI (machine learning, deep learning) applications in drug toxicity prediction.
  • Analysis of toxicity databases and their role in predictive modeling.
  • In-depth examination of prediction results and methodologies for various toxicity endpoints (e.g., acute toxicity, carcinogenicity, organ-specific toxicity).

Main Results:

  • AI, particularly deep learning and multimodal data fusion, is significantly advancing drug toxicology assessment.
  • Various AI models have demonstrated efficacy in predicting diverse toxicity endpoints.
  • The review synthesizes current research, highlighting successful applications and identifying key trends.

Conclusions:

  • AI presents a powerful paradigm shift, enhancing the efficiency and reliability of drug toxicity prediction.
  • AI technologies offer actionable strategies for improving drug safety assessment in development pipelines.
  • Continued research and development in AI are crucial for future advancements in predictive toxicology.