FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive toxicity, but none of them make full use of the topological information of compounds.

Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.

Background: Chemicals may lead to acute liver injuries, posing a serious threat to human health. Achieving the precise safety profile of a compound is challenging due to the complex and expensive testing procedures. In silico approaches will aid in identifying the potential risk of drug candidates in the initial stage of drug development and thus mitigating the developmental cost.

Occurrence and ecological risk assessment of 16 phthalates in surface water of the mainstream of the Yangtze River, China.

Phthalic acid esters (PAEs), a class of typical endocrine disruptors, have received considerable attention due to their widespread applications and adverse effects on biological health. In this study, 30 water samples, along the mainstream of the Yangtze River (YR), were collected from Chongqing (upper stream) to Shanghai (estuary) from May to June in 2019. The total concentrations of 16 targeted PAEs ranged from 0.