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

Artificial neural networks (ANNs) have a huge potential in toxicology research. They may be
used to predict toxicity of various chemical compounds or classify the compounds based on …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

Although there has been considerable progress in molecular property prediction in
computer-aided drug design, there is a critical need to have fast and accurate models. Many …

In the modern “omics” era, measurement of the human exposome is a critical missing link
between genetic drivers and disease outcomes. High-resolution mass spectrometry …

Artificial intelligence (AI) has permeated various sectors, including the pharmaceutical
industry and research, where it has been utilized to efficiently identify new chemical entities …

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect
thousands of organic substances in environmental samples. However, new strategies are …

Occupational injuries and toxicant exposures lead to the development of neuroinflammation
by activating distinct mechanistic signaling cascades that ultimately culminate in the …

The recent synergy of machine learning (ML) with molecular synthesis has emerged as an
increasingly powerful platform in organic synthesis and catalysis. This merger has set the …

Mitochondrial toxicity is an important safety endpoint in drug discovery. Models based solely
on chemical structure for predicting mitochondrial toxicity are currently limited in accuracy …