Artificial Intelligence Approaches in Toxicology

Abstract

Artificial Intelligence (AI) is transforming toxicology by improving the efficiency and accuracy of chemical and drug hazardous impact prediction, detection, and analysis. Large datasets from genomes, proteomics, and chemical structure databases are being used by AI-driven models to augment or replace traditional toxicological evaluations, which frequently depend on animal testing and in vitro techniques. Early toxicity, dose-response, and mechanism of action prediction is made possible by machine learning algorithms' ability to recognise intricate patterns in biological reactions. The sensitivity and specificity of toxicological predictions are being increased by modelling nonlinear interactions and high-dimensional data using deep learning approaches, such as neural networks. AI also makes it easier to use computational toxicology and high-throughput screening methods, which lessen the need for intensive laboratory testing and are consistent with the 3Rs (Replacement, Reduction, and Refinement) in animal research. Furthermore, natural language processing helps to glean important information from extensive toxicology studies and scientific literature. These developments help with drug research, environmental safety evaluations, regulatory decision-making, and personalised medicine. Notwithstanding notable advancements, issues with data quality, model transparency, and regulatory acceptability still exist. Continued use of AI in toxicology holds the potential to improve scientific ethics and efficiency, reduce exposure to hazardous compounds in humans and the environment, and speed up risk assessment procedures.