ArticleChemical research in toxicology2020
Predictive Models for Human Organ Toxicity Based on
Article in Chemical research in toxicology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Expanded Tox21 Biological Assay Panel for the Prediction of Drug-Induced Liver Injury and Cardiotoxicity.Environmental health perspectives · 2026Article
- 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
- Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.Communications chemistry · 2025Article
- Novel target identification towards drug repurposing based on biological activity profiles.PloS one · 2025Article
- Predicting seizure liability of small molecules using an in vitro multi-electrode array based assay coupled with modeling of brain disposition.Current research in toxicology · 2025Article
- Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.Toxicology and applied pharmacology · 2024Article
- Advancing Toxicity Predictions: A Review onEnvironment & health (Washington, D.C.) · 2024Review
- A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- Unleashing the potential of cell painting assays for compound activities and hazards prediction.Frontiers in toxicology · 2024Review
- Use ofFrontiers in toxicology · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review.Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association · 2023Review
- Data-Driven Quantitative Structure-Activity Relationship Modeling for Human Carcinogenicity by Chronic Oral Exposure.Environmental science & technology · 2023Article
- Identification of Potent and Selective Acetylcholinesterase/Butyrylcholinesterase Inhibitors by Virtual Screening.Journal of chemical information and modeling · 2023Article
- Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates.Journal of chemical information and modeling · 2023Article
- Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.Toxicology and applied pharmacology · 2022Article
- Efficient Identification of Anti-SARS-CoV-2 Compounds Using Chemical Structure- and Biological Activity-Based Modeling.Journal of medicinal chemistry · 2022Article
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Article
- ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities.Journal of chemical information and modeling · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with the increasing numbers of chemicals that humans are exposed to in the environment. The purpose of this investigation was to build optimal prediction models for various human
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.