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

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.