ArticleToxicology research2020
Article in Toxicology research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Single-cell morphological responses as predictors of ciprofloxacin resistance inMicrobiology spectrum · 2026Article
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Comprehensive Review on the Toxicity of Five Main AQ Constituents from Rhubarb: Mechanisms, Challenges and Future Perspectives.Drug design, development and therapy · 2026Review
- ENOblock synergizes with colistin to treat Acinetobacter baumannii infections.EMBO molecular medicine · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Research trends of computational toxicology: a bibliometric analysis.Toxicology research · 2024Review
- Article
- Developmental neurotoxicity (DNT) QSAR combination prediction model establishment and structural characteristics interpretation.Toxicology research · 2024Article
- High-Resolution Bacterial Cytological Profiling Reveals Intrapopulation Morphological Variations upon Antibiotic Exposure.Antimicrobial agents and chemotherapy · 2023Article
- Brain physiome: A concept bridgingBioactive materials · 2022Review
- SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.Frontiers in chemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Neurotoxicity is one of the main causes of drug withdrawal, and the biological experimental methods of detecting neurotoxic toxicity are time-consuming and laborious. In addition, the existing computational prediction models of neurotoxicity still have some shortcomings. In response to these shortcomings, we collected a large number of data set of neurotoxicity and used PyBioMed molecular descriptors and eight machine learning algorithms to construct regression prediction models of chemical neurotoxicity. Through the cross-validation and test set validation of the models, it was found that the extra-trees regressor model had the best predictive effect on neurotoxicity ([Formula: see text] = 0.784). In addition, we get the applicability domain of the models by calculating the standard deviation distance and the lever distance of the training set. We also found that some molecular descriptors are closely related to neurotoxicity by calculating the contribution of the molecular descriptors to the models. Considering the accuracy of the regression models, we recommend using the extra-trees regressor model to predict the chemical autonomic neurotoxicity.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.