Abstract
Artificial intelligence (AI) contributes to various fields of science and technology, such as improving the efficiency of research and development and creating new methods. In the field of toxicology, AI such as machine learning are also expected to find toxicity-related information from accumulated large-scale experimental data, which will be useful in evaluating the safety of chemical substances. Machine learning is a technology that allows computers to discover patterns hidden in data and make predictions about new data. Technological progress in machine learning, including deep learning, has been remarkable, and it is now possible to handle unstructured data such as images and text. In addition, generative AI and explainable AI are also attracting attention. In the field of toxicology, there are situations in which it is difficult to utilize AI. Still, the foundations for accelerating the use of AI are being laid, such as the publication of high-throughput screening data and approach to few data. However, when utilizing AI technology, it is important to consider data quality, model reproducibility, and interpretability. Our laboratory focuses on toxicity-related databases and machine learning from the perspective of regulatory science. In this workshop, I will introduce skin sensitization prediction models and side effect prediction models in humans.
