Author: Xin Zhang
Environmental Wind is an advanced generative large language model (LLM)-based human-computer interaction system, designed to provide comprehensive predictions and solutions in the fields of environmental exposure, pollutant identification, toxicological mechanism research, and public health.
Author: Jiaqi Luo
Vreact is a predictive model based on graph deep learning, designed to accurately estimate the atmospheric oxidation reaction rate constants of VOCs with four oxidants.
Author: Lingjing Zhang
ChemNTP is siamese neural network approach for predicting 199 neurotoxicity related targets of environmental chemicals, based on the primary structure of proteins and the molecular structures.
Author: Xin Zhang, Xiaoxiao Han
MSFragTox, a novel method leveraging high-resolution mass spectrometry (HRMS/MS) data, directly predicts seven endocrine disruption-related toxicities
Author: Lu Zhao
Our research presents the CatNet model, a novel advance that synergistically integrates chemical and protein information to predict interactions between chemicals and NRs. What sets CatNet apart is its scalability and ability to maintain accuracy even on uncharted NR territories.
Author: Yuxing Hao
Our research presents a neurotoxicity prediction model that integrates compound data with visualization analysis, aiding in the evaluation of chemicals and supporting environmental health risk assessments.
Author: Yuxing Hao
This study proposes models for predicting acute toxicity of compounds and conducting risk assessment based on the GHS, which contribute to filling the data gap in chemical acute toxicity and enable a tiered evaluation of acute toxicity risks.