ArticleToxicology research2024
Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation.
Article in Toxicology research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 1 citations in OpenAlex.
- A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.RSC advances · 2026Article
- Article
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7 authors at 1 institution in 1 country.
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Abstract
With the incidence of neurodevelopmental disorders on the rise, it is imperative to screen and evaluate developmental neurotoxicity (DNT) compounds from a large number of environmental chemicals and understand their mechanisms. In this study, DNT qualitative structure-activity relationship (QSAR) study was carried out for the first time based on DNT data of mammals and structural characterization of DNT compounds was preliminarily illustrated. Five different classification algorithms and two feature selection methods were used to construct prediction models. The best model had good predictive ability on the external test set, but a small application domain (AD). Through combining of three different models, both MCC and AD values were improved. Furthermore, electronical properties, van der Waals volume-related properties and S, Cl or P containing substructure were found to be associated with DNT through modeling descriptors analysis and structure alerts (SAs) identification. This study lays a foundation for further DNT prediction of environmental exposures in human and contributes to the understanding of DNT mechanism.
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