AI in Predictive Toxicology

Abstract

The field of toxicology is undergoing a significant transformation due to the integration of artificial intelligence (AI). In addition to traditional reliance on empirical studies and animal testing, AI-powered predictive toxicology is now used to predict the toxic effects of chemicals and drugs. This chapter examines the role of AI in enhancing the accuracy, efficiency, and breadth of toxicological assessments by bridging the gap between traditional approaches and advanced AI techniques. It explores various AI methodologies, such as machine learning, deep learning, and neural networks, focusing on their application in toxicity prediction. Furthermore, this chapter investigates the integration of AI with toxicological databases and the development and empirical validation of predictive models. It also addresses various challenges associated with AI-powered toxicology, including data quality, model interpretability, and scalability. The chapter concludes that despite facing challenges, AI is a powerful tool in modern toxicological analysis.