Artificial intelligence (AI) has the potential to accelerate drug development at every stage of the process including drug safety. By leveraging machine learning, we could reduce the reliance on animal testing, lower development costs, and discover safer drug candidates. This presentation will examine how AI and machine learning methodologies are being applied in the identification and assessment of toxicity early in the drug discovery pipeline. It will explore applications in virtual screening, high-throughput screening data analysis, and the development of more predictive in vitro and in vivo models. This presentation will also discuss the challenges and limitations of AI and the future applications of this technology in advancing drug discovery toxicology.