Integrating molecular fingerprints with machine learning for accurate neurotoxicity prediction: an observational study
Yilin Gao, Junjin Mu, Kaifeng Liu, Min Wang · Advanced technology in neuroscience . · 2025
JOURNAL/atin/04.03/02274269-202509000-00002/figure1/v/2026-04-23T113529Z/r/image-tiff Neurotoxicity refers to harmful changes in the structure and function of the central and/or peripheral nervous system caused by exposure to chemical, physical, or biological factors. These changes can manifest as organic damage, functional disorders, and behavioral alterations. Traditional biological testing methods for neurotoxicity are often expensive, labor-intensive, and time-consuming. Additionally, existing predictive models rely on small datasets and lack comprehensive, large-scale, and professional algorithms and platforms for accurately predicting brain toxicity. In this study, we integrate molecular fingerprints and descriptors—namely, MorganFP, MACCS, RDKFP, and TopologicalTorsionFP—with machine learning models such as Random Forest and Support Vector Machine, as well as deep learning models such as Graph Neural Networks, to develop a neurotoxicity prediction model. The optimal model, MorganFP-SVM, demonstrates excellent performance in predicting neurotoxicity, achieving an accuracy of 86.56%. This significantly outperforms other neurotoxicity prediction models, including ADMETlab 3.0. When compared to existing neurotoxicity prediction models DINeuroT and ADMETlab 3.0, the MorganFP-SVM model exhibits superior performance across multiple key evaluation metrics, offering greater balance and comprehensiveness as a reliable tool for neurotoxicity prediction. The DINeuroT model shows weaknesses in specificity and Matthews correlation coefficient, while ADMETlab 3.0, though strong in sensitivity, has limited capacity to accurately identify negative samples. Overall, the comprehensive advantages of the MorganFP-SVM model in neurotoxicity prediction establish it as an essential tool in the field. It not only provides higher predictive accuracy but also demonstrates strong stability and reliability across various evaluation metrics. This model offers a promising approach for assessing neurotoxicity risks related to drug development and environmental pollutants, thereby providing a scientific basis for public health decision-making.