ReviewCurrent topics in medicinal chemistry2014
Integrative approaches for predicting in vivo effects of chemicals from their structural descriptors and the results of short-term biological assays.
Review in Current topics in medicinal chemistry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 16 citations in OpenAlex.
- Towards the sustainable discovery and development of new antibiotics.Nature reviews. Chemistry · 2021Review
- Article
- Leveraging heterogeneous data from GHS toxicity annotations, molecular and protein target descriptors and Tox21 assay readouts to predict and rationalise acute toxicity.Journal of cheminformatics · 2019Article
- Conditional Toxicity Value (CTV) Predictor: AnEnvironmental health perspectives · 2018Article
- The Impact of Novel Assessment Methodologies in Toxicology on Green Chemistry and Chemical Alternatives.Toxicological sciences : an official journal of the Society of Toxicology · 2018Review
- Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- A chemical-biological similarity-based grouping of complex substances as a prototype approach for evaluating chemical alternatives.Green chemistry : an international journal and green chemistry resource : GC · 2016Article
- Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization.Nature communications · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical 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.