ChemNTP: Advanced Prediction of Neurotoxicity Targets for Environmental Chemicals Using a Siamese Neural Network

作者：Lingjing Zhang, Tingji Yao, Jiaqi Luo, Hang Yi, Xiaoxiao Han, Wenxiao Pan, Qiao Xue, Xian Liu, Jianjie Fu, Aiqian Zhang · 发表于：Environmental Science & Technology · 年份：2024 · DOI：10.1021/acs.est.4c10081 · 被引用次数：12 · 研究领域：Computational Drug Discovery Methods、Cholinesterase and Neurodegenerative Diseases、Chemistry and Chemical Engineering

Environmental chemicals can enter the human body through various exposure pathways, potentially leading to neurotoxic effects that pose significant health risks. Many such chemicals have been identified as neurotoxic, but the molecular mechanisms underlying their toxicity, including specific binding targets, remain unclear. To address this, we developed ChemNTP, a predictive model for identifying neurotoxicity targets of environmental chemicals. ChemNTP integrates a comprehensive representation of chemical structures and biological targets, improving upon traditional methods that are limited to single targets and mechanisms. By leveraging these structural representations, ChemNTP enables rapid screening across 199 potential neurotoxic targets or key molecular initiating events (MIEs). The model demonstrates robust predictive performance, achieving an area under the receiver operating characteristic curve ( AUC ROC ) of 0.923 on the test set. Additionally, ChemNTP’s attention mechanism highlights critical residues in binding targets and key functional groups or atoms in molecules, offering insights into the structural basis of interactions. Experimental validation through in vitro enzyme activity assays and molecular docking confirmed the binding of eight polybrominated diphenyl ethers (PBDEs) to acetylcholinesterase (AChE). We also provide a user-friendly software interface to facilitate the rapid identification of neurotoxicity targets for emerging environmental pollutants, ...