The toxic effects of compounds on environment, humans, and other organisms have been a
major focus of many research areas, including drug discovery and ecological research …

Selecting a model in predictive toxicology often involves a trade-off between prediction
performance and explainability: should we sacrifice the model performance to gain …

Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high
attrition rates in later development stages. Toxicity has been one of the main causes of …

Machine learning (ML) models to predict the toxicity of small molecules have garnered great
attention and have become widely used in recent years. Computational toxicity prediction is …

Toxicity prediction is very important to public health. Among its many applications, toxicity
prediction is essential to reduce the cost and labor of a drug's preclinical and clinical trials …

Abstract Machine learning and artificial intelligence approaches have revolutionized
multiple disciplines, including toxicology. This review summarizes representative recent …

The recent advancements in toxicogenomics have led to the availability of large omics data
sets, representing the starting point for studying the exposure mechanism of action and …

Toxicity prediction is a critical step in the drug discovery process that helps identify and
prioritize compounds with the greatest potential for safe and effective use in humans, while …

To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo
testing for many toxic endpoints, resulting in millions of animal experiments conducted each …

Xenobiotic metabolism has evolved as a key protective system of organisms against
potentially harmful chemicals or compounds typically not present in a particular organism …