Advances in Artificial Intelligence and Machine Learning for Toxicity Prediction in Computational Toxicology: A Comprehensive Review.

Over the past three decades, artificial intelligence (AI) and machine learning (ML) have revolutionized computational toxicology, providing powerful tools for predicting chemical toxicity and supporting safer assessments for human health and the environment. This review offers a critical 30-year synthesis (1995-2025) that distinguishes itself from narrower prior works through its interdisciplinary integration of historical evolution, multi-omics data fusion, nanotoxicity challenges, regulatory frameworks, multi-stakeholder perspectives, and emerging hybrid and generative models. Key findings reveal a clear progression: from early artificial neural networks capturing non-linear patterns in the 1990s to modern deep learning architectures such as convolutional neural networks and graph neural networks that have achieved over 85% accuracy, primarily in retrospective benchmarks on ToxCast and Tox21 datasets for endpoints including hepatotoxicity, cardiotoxicity, and nanotoxicity. However, prospective validation on novel compounds remains limited, representing a critical translational gap. Traditional machine learning methods (random forests and support vector machines) effectively handle imbalanced high-throughput screening data, facilitating multi-omics integration and applications across pharmaceuticals, pesticides, cosmetics, and nanoparticles. These approaches strengthen read-across strategies, Integrated Approaches to Testing and Assessment (IATA), and Threshold of Toxicological Concern (TTC) frameworks. Regulatory acceptance, guided by OECD principles, increasingly emphasizes explainable AI to ensure transparency and validation.In conclusion, AI/ML approaches can substantially reduce animal testing, accelerate safety evaluations, and address data gaps, yet require ongoing attention to biases, model opacity, and limited prospective performance. Hybrid mechanistic-AI models, federated learning, and strengthened cross-sector collaboration represent the most promising path forward.