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

• Published in: Environmental science & technology 2024-12, Vol.58 (51), p.22646-22656
• Main Authors: Zhang, Lingjing, Yao, Tingji, Luo, Jiaqi, Yi, Hang, Han, Xiaoxiao, Pan, Wenxiao, Xue, Qiao, Liu, Xian, Fu, Jianjie, Zhang, Aiqian
• Format: Article
• Language: English
• Subjects: Acetylcholinesterase; Artificial neural networks; Atomic structure; Binding; Chemicals; Data Science; Environmental Pollutants - toxicity; Enzymatic activity; Enzyme activity; Ethers; Functional groups; Health risks; Humans; Molecular docking; Molecular Docking Simulation; Molecular modelling; Neural networks; Neural Networks, Computer; Neurotoxicity; Neurotoxicity Syndromes; Polybrominated diphenyl ethers; Prediction models; Predictions; Representations; Structure-function relationships; Toxicity
• ISSN: 0013-936X; 1520-5851; 1520-5851
• DOI: 10.1021/acs.est.4c10081

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, with potential applications in studying MIEs for more types of toxicity.