ArticleInternational journal of molecular sciences2022
Prediction of the Neurotoxic Potential of Chemicals Based on Modelling of Molecular Initiating Events Upstream of the Adverse Outcome Pathways of (Developmental) Neurotoxicity.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 32 citations in OpenAlex.
- Decoding the Neuroinflammatory Potential of Non-Nutritive Sweeteners Through An Integrative Computational Approach.Neurotoxicity research · 2026Article
- A benchmark dataset and interpretable deep learning framework for drug-induced developmental neurotoxicity prediction.RSC advances · 2026Article
- A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.Journal of cheminformatics · 2026Article
- Deep learning-enhanced QSAR modeling for predicting developmental neurotoxicity based on molecular initiating events from adverse outcome pathways.Molecular diversity · 2026Article
- Combined Modeling Approaches for Assessing Sodium-Iodide Symporter Inhibition.Journal of chemical information and modeling · 2026Article
- Review
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity.Journal of cheminformatics · 2024Article
- ProTox 3.0: a webserver for the prediction of toxicity of chemicals.Nucleic acids research · 2024Article
- Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation.Toxicology research · 2024Article
- New approach methods to assess developmental and adult neurotoxicity for regulatory use: a PARC work package 5 project.Frontiers in toxicology · 2024Article
- Structural Insights into Neonicotinoids and N-Unsubstituted Metabolites on Human nAChRs by Molecular Docking, Dynamics Simulations, and Calcium Imaging.International journal of molecular sciences · 2023Article
- Applications and Potential of In Silico Approaches for Psychedelic Chemistry.Molecules (Basel, Switzerland) · 2023Review
- In silico prediction of parkinsonian motor deficits-related neurotoxicants based on the adverse outcome pathway concept.Archives of toxicology · 2022Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
Developmental and adult/ageing neurotoxicity is an area needing alternative methods for chemical risk assessment. The formulation of a strategy to screen large numbers of chemicals is highly relevant due to potential exposure to compounds that may have long-term adverse health consequences on the nervous system, leading to neurodegeneration. Adverse Outcome Pathways (AOPs) provide information on relevant molecular initiating events (MIEs) and key events (KEs) that could inform the development of computational alternatives for these complex effects. We propose a screening method integrating multiple Quantitative Structure-Activity Relationship (QSAR) models. The MIEs of existing AOP networks of developmental and adult/ageing neurotoxicity were modelled to predict neurotoxicity. Random Forests were used to model each MIE. Predictions returned by single models were integrated and evaluated for their capability to predict neurotoxicity. Specifically, MIE predictions were used within various types of classifiers and compared with other reference standards (chemical descriptors and structural fingerprints) to benchmark their predictive capability. Overall, classifiers based on MIE predictions returned predictive performances comparable to those based on chemical descriptors and structural fingerprints. The integrated computational approach described here will be beneficial for large-scale screening and prioritisation of chemicals as a function of their potential to cause long-term neurotoxic effects.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.