Background Bioactive factors in functional foods play a crucial role in performing their
specific functions. These factors have their own specific physical and chemical properties …

Abstract Machine learning (ML) is becoming capable of transforming biomolecular
interaction description and calculation, promising an impact on molecular and drug design …

There are currently 85,000 chemicals registered with the Environmental Protection Agency
(EPA) under the Toxic Substances Control Act, but only a small fraction have measured …

Progesterone receptors (PRs) are implicated in various cancers since their
presence/absence can determine clinical outcomes. The overstimulation of progesterone …

Ubiquitous chemicals in the environment may pose a threat to human health and the
ecosystem, so comprehensive toxicity information must be obtained. Due to the inability of …

The interaction of nuclear receptors (NRs) with chemical compounds can cause
dysregulation of endocrine signaling pathways, leading to adverse health outcomes due to …

The toxicity, absorption, distribution, metabolism, and excretion properties of some targets
are difficult to predict by quantitative structure–activity relationship analysis. Therefore, there …

Some targets predicted by machine learning (ML) in drug discovery remain a challenge
because of poor prediction. In this study, a new prediction model was developed and rat …

A deep learning-based quantitative structure–activity relationship analysis, namely the
molecular image-based DeepSNAP–deep learning method, can successfully and …

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that senses
environmental exogenous and endogenous ligands or xenobiotic chemicals. In particular …