Drug-induced rhabdomyolysis (DIR) is a serious adverse reaction and can be fatal. In the present study, we focused on the modeling and understanding of the molecular basis of DIR of small molecule drugs. A series of machine-learning models were devel...
The prediction of compound cytotoxicity is an important part of the drug discovery process. However, it usually appears as poor predictive performance because the datasets are high-throughput and have a class-imbalance problem. In this study, several...
Reproductive toxicity is an important regulatory endpoint in health hazard assessment. Because the in vivo tests are expensive, time consuming and require a large number of animals, which must be killed, in silico approaches as the alternative strate...
Drug-induced liver injury (DILI) is the most common cause of acute liver failure and often responsible for drug withdrawals from the market. Clinical manifestations vary, and toxicity may or may not appear dose-dependent. We present several machine-l...
It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathwa...
The replacement of animal use in testing for regulatory classification of skin sensitizers is a priority for US federal agencies that use data from such testing. Machine learning models that classify substances as sensitizers or non-sensitizers witho...
Supervised learning methods promise to improve integrated testing strategies (ITS), but must be adjusted to handle high dimensionality and dose-response data. ITS approaches are currently fueled by the increasing mechanistic understanding of adverse ...
The skin sensitization potential of chemicals has been determined with the use of the murine local lymph node assay (LLNA). However, in recent years public concern about animal welfare has led to a requirement for non-animal risk assessment systems f...