Opportunities for Computational Modeling in Toxicology Prediction
Luis G. Valerio · 2024
Computational modeling has emerged as a promising technological tool for toxicology prediction, providing a cost-effective, rapid, and high-throughput method for predicting the toxicity of chemicals. With the growing concern over the use of animal testing and the safety of chemicals used in various industries and the availability of machine learning algorithms even for non-specialists in the field, this chapter discusses the areas of opportunity for computational modeling in the field of toxicology. The development of in silico models based on artificial intelligence using mainly machine learning has shown considerable promise in predicting toxicity. The use of in vitro high-throughput screening technologies has enabled the rapid and efficient toxicity assessment of large numbers of compounds, facilitating the creation of large libraries for the discovery and development of robust predictive models. This chapter discusses opportunities for computational modeling in toxicology prediction. With the rapid growth of artificial intelligence systems, the development of increasingly sophisticated modeling techniques applied to issues in toxicology has become a reality. The chapter discusses the numerous opportunities for computational modeling to have an impact on toxicology and discusses how modeling is poised to play a critical role in the assessment of chemical safety and risk in the years to come.