Carcinogenicity assessment of any compound is a laborious and expensive exercise with
several associated ethical and practical concerns. While artificial intelligence (AI) offers …

Carcinogenicity testing plays an essential role in identifying carcinogens in environmental
chemistry and drug development. However, it is a time-consuming and label-intensive …

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats
owing to its constant exposure to a myriad of heterogeneous compounds. Despite the …

A wide variety of artificial intelligence (AI) and structure-activity relationship (SAR)
approaches have been applied to tackling the general problem of predicting rodent …

In vitro genotoxicity bioassays are cost-efficient methods of assessing potential carcinogens.
However, many genotoxicity bioassays are inappropriate for detecting chemicals eliciting …

Cancer remains a significant global health concern, with millions of deaths attributed to it
annually. Environmental pollutants play a pivotal role in cancer etiology and contribute to the …

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 …

The availability of large in vitro datasets enables better insight into the mode of action of
chemicals and better identification of potential mechanism (s) of toxicity. Several studies …

Using a benchmark Ames mutagenicity data set, we evaluated the performance of molecular
fingerprints as descriptors for developing quantitative structure–activity relationship (QSAR) …

Introduction This review explores the transformative impact of machine learning (ML) on
carcinogenicity prediction within drug development. It discusses the historical context and …