Recently, research on the development of artificial intelligence (AI)-based computational
toxicology models that predict toxicity without the use of animal testing has emerged …

The complexity and dynamics of the environment make it extremely difficult to directly predict
and trace the temporal and spatial changes in pollution. In the past decade, the …

Endocrine disruptors are hazardous chemicals with chronic health effects for most living
organisms, inducing homeostasis, hormonal imbalances, cancer, reproductive and …

Computational pharmacology and chemistry of drug-like properties along with
pharmacokinetic studies have made it more amenable to decide or predict a potential drug …

The brain neurons' electrical activities represented by Electroencephalogram (EEG) signals
are the most common data for diagnosing Epilepsy seizure, which is considered a chronic …

Traditional methodologies for assessing chemical toxicity are expensive and time-
consuming. Computational modeling approaches have emerged as low-cost alternatives …

Environmental hazard assessments are reliant on toxicity data that cover multiple organism
groups. Generating experimental toxicity data is, however, resource-intensive and time …

Traditional experimental testing to identify endocrine disruptors that enhance estrogenic
signaling relies on expensive and labor-intensive experiments. We sought to design a …

Develo** mechanistic non-animal testing methods based on the adverse outcome
pathway (AOP) framework must incorporate molecular and cellular key events associated …

Per-and polyfluoroalkyl substances (PFAS) are a class of environmental toxicants, and
some, such as perfluorooctanesulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) …