Chemical neurotoxicity remains a critical safety concern in the domains of drug development and environmental risk assessment. In these contexts, reliable early stage prediction can significantly reduce experimental costs. In this study, we developed NeuroTDPi, a multilayer fully connected deep neural network model designed to identify neurotoxic compounds. The model employs a multimodal fusion strategy, integrating molecular characterization with feature representations tailored to three specific neurotoxicity end points: blood-brain barrier permeability, neuronal toxicity, and mammalian neurotoxicity. In order to enhance the interpretability of the model, the SHapley Additive Explanations (SHAP) method was employed to elucidate the contributions of various physical and chemical properties. NeuroTDPi exhibited a commendable performance, attaining area under the receiver operating characteristic curve values of 0.97, 0.84, and 0.82 for the three end points, respectively. Furthermore, a comprehensive mining and visualization workflow identified structural alerts associated with neurotoxicity, offering mechanistic insights into the observed toxic effects. These resources, which provide a robust platform for neurotoxicity evaluation and actionable structural insights for risk assessment, are freely available at https://www.sapredictor.cn/.
Liu et al. (Thu,) studied this question.