ArticlePloS one2013
Predicting chemical toxicity effects based on chemical-chemical interactions.
Article in PloS one, 2013. 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.
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations.Scientific reports · 2023Article
- Deep learning integration of molecular and interactome data for protein-compound interaction prediction.Journal of cheminformatics · 2021Article
- Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure: Opportunities for Biomonitoring and Health-Related Research.Environmental health perspectives · 2019Article
- Prediction of drug indications based on chemical interactions and chemical similarities.BioMed research international · 2015Article
- Identification of Chemical Toxicity Using Ontology Information of Chemicals.Computational and mathematical methods in medicine · 2015Article
- Beyond new chemical entities: advancing drug development based on functional versatility of antibodies.Human vaccines & immunotherapeutics · 2014Article
- Article
- Integration of bioinformatics to biodegradation.Biological procedures online · 2014Review
- Identifying chemicals with potential therapy of HIV based on protein-protein and protein-chemical interaction network.PloS one · 2013Article
- Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.BMC medical genomics · 2013Article
- Advanced systems biology methods in drug discovery and translational biomedicine.BioMed research international · 2013Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Toxicity is a major contributor to high attrition rates of new chemical entities in drug discoveries. In this study, an order-classifier was built to predict a series of toxic effects based on data concerning chemical-chemical interactions under the assumption that interactive compounds are more likely to share similar toxicity profiles. According to their interaction confidence scores, the order from the most likely toxicity to the least was obtained for each compound. Ten test groups, each of them containing one training dataset and one test dataset, were constructed from a benchmark dataset consisting of 17,233 compounds. By a Jackknife test on each of these test groups, the 1(st) order prediction accuracies of the training dataset and the test dataset were all approximately 79.50%, substantially higher than the rate of 25.43% achieved by random guesses. Encouraged by the promising results, we expect that our method will become a useful tool in screening out drugs with high toxicity.
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.