Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …

Deep learning has remarkably impacted several different scientific disciplines over the last
few years. For example, in image processing and analysis, deep learning algorithms were …

To achieve water quality objectives of the zero pollution action plan in Europe, rapid
methods are needed to identify the presence of toxic substances in complex water samples …

Drug toxicity prediction is an important step in ensuring patient safety during drug design
studies. While traditional preclinical studies have historically relied on animal models to …

In recent times, machine learning has become increasingly prominent in predictive
toxicology as it has shifted from in vivo studies toward in silico studies. Currently, in vitro …

In the late 2010s, artificial intelligence (AI) technologies became complementary to the
research areas of food science and nutrition. This review aims to summarize these …

The rapid progress of AI impacts diverse scientific disciplines, including toxicology, and has
the potential to transform chemical safety evaluation. Toxicology has evolved from an …

Predictive modeling for toxicity can help reduce risks in a range of applications and
potentially serve as the basis for regulatory decisions. However, the utility of these …

The wide application of nanomaterials in consumer and medical products has raised
concerns about their potential adverse effects on human health. Thus, more and more …

Explainable machine learning for molecular toxicity prediction is a promising approach for
efficient drug development and chemical safety. A predictive ML model of toxicity can reduce …