Artificial intelligence (AI) has recently emerged as a unique developmental influence that is
playing an important role in the development of medicine. The AI medium is showing the …

Predicting the blood–brain barrier (BBB) permeability of small-molecule compounds using a
novel artificial intelligence platform is necessary for drug discovery. Machine learning and a …

Adverse drug reactions continue to be not only one of the most urgent problems in clinical
medicine, but also a social problem. The aim of this study was a bibliometric analysis of the …

It is imperative to comprehend the mechanisms that underlie drug toxicity in order to
enhance the efficacy and safety of novel therapeutic agents. The capacity to identify …

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design
process. Although conventional machine learning techniques have shown some success in …

In recent years, with the rapid development of society, organic compounds have been
released into aquatic environments in various forms, posing a significant threat to the …

Introduction Social media platforms serve as a valuable resource for users to share health-
related information, aiding in the monitoring of adverse events linked to medications and …

Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the
toxicity of municipal wastewater through dose–effect curves is challenging owing to the time …

Exposure to toxic chemicals threatens species and ecosystems. This study introduces a
novel approach using Graph Neural Networks (GNNs) to integrate aquatic toxicity data …

Antiviral drugs are crucial for managing viral infections, but current treatment options remain
limited, particularly for emerging viruses. These drugs can be classified based on their …