ReviewFrontiers in chemistry2018
Review in Frontiers in chemistry, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 77 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
77 citing papers in PubMed.
- An Integrated in Silico, in Vitro, and in Vivo Assessment of the Antioxidant and Neuroprotective Profile of Selenocyanate Compounds.Biological trace element research · 2026Article
- ToxiVerse: chemical bioprofiling, toxicity data sharing and customizable predictive modeling.Bioinformatics (Oxford, England) · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Ligand-agnostic off-target site prediction for early toxicity screening by leveraging point cloud-based protein cavity analysis.Briefings in bioinformatics · 2026Article
- ToxiVerse: A Public Platform for Chemical Toxicity Data Sharing and Customizable Predictive Modeling.bioRxiv : the preprint server for biology · 2026Article
- Qualitative and quantitative in silico toxicity profiling of "angel dust": phencyclidine (PCP) analogues as new psychoactive substances (3-HO-PCP, 3-MeO-PCP, 4-MeO-PCP, 3-HO-PCE, 3-MeO-PCE, 4-MeO-PCE).Archives of toxicology · 2026Article
- Design of inhibitors to Klebsiella pneumoniae aspartate semialdehyde dehydrogenase towards hospital-acquired infections.Molecular diversity · 2026Article
- A comprehensive evaluation of advanced methods for identifying structural alerts using extensive toxicity data.Journal of cheminformatics · 2026Article
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- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- An Imbalance Regression Approach to Toxicity Prediction of Chemicals for Potential Use in Environmentally Acceptable Lubricants.ACS applied materials & interfaces · 2025Article
- Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners.Applied biochemistry and biotechnology · 2025Review
- High-throughput screening of natural antiviral drug candidates against white spot syndrome virus targeting VP28 in Penaeus monodon: Computational drug design approaches.Journal, genetic engineering & biotechnology · 2025Article
- Efficient and Explainable Virtual Screening of Molecules through Fingerprint-Generating Networks Integrated with Artificial Neural Networks.ACS omega · 2025Article
- Thiophene-based water-soluble CBiomaterials translational · 2025Article
- Assessment of the Anti-adipogenic Effect ofCurrent pharmaceutical biotechnology · 2025Article
- QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.ACS omega · 2024Article
- Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy.Toxics · 2024Review
- Towards a comprehensive assessment of QSP models: what would it take?Journal of pharmacokinetics and pharmacodynamics · 2024Review
17 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issuedCorrigendum:2018
Authors and funding
5 authors.
Funding
Abstract
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
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.