ArticleJournal of chemical information and modeling2017
Predicting Drug-Induced Cholestasis with the Help of Hepatic Transporters-An in Silico Modeling Approach.
Article in Journal of chemical information and modeling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Integration of in vitro and in silico approaches enables prediction of drug-induced liver injury.Archives of toxicology · 2026Article
- vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.bioRxiv : the preprint server for biology · 2026Article
- A multi-dimensional computational framework of drug-induced hepatotoxicity: integrating molecular structure features with disease pathogenesis.Briefings in bioinformatics · 2025Article
- Refining Drug-Induced Cholestasis Prediction: An Explainable Consensus Model Integrating Chemical and Biological Fingerprints.Journal of chemical information and modeling · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- Drug-induced cholestasis (DIC) predictions based on in vitro inhibition of major bile acid clearance mechanisms.Archives of toxicology · 2025Article
- MONSTROUS: a web-based chemical-transporter interaction profiler.Frontiers in pharmacology · 2025Article
- Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.Journal of chemical information and modeling · 2024Article
- InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism.Journal of cheminformatics · 2024Article
- Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling.Current drug research reviews · 2024Review
- Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors.ACS omega · 2023Article
- Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay.Journal of hazardous materials · 2022Article
- In Silico Models for Hepatotoxicity.Methods in molecular biology (Clifton, N.J.) · 2022Review
- FP-ADMET: a compendium of fingerprint-based ADMET prediction models.Journal of cheminformatics · 2021Article
- Random forest classification for predicting lifespan-extending chemical compounds.Scientific reports · 2021Article
- Current Models for Predicting Drug-induced Cholestasis: The Role of Hepatobiliary Transport System.Iranian journal of pharmaceutical research : IJPR · 2021Review
- Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).Molecular pharmaceutics · 2020Article
- Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset.International journal of molecular sciences · 2020Article
- Review
- Metformin Disrupts Bile Acid Efflux by Repressing Bile Salt Export Pump Expression.Pharmaceutical research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Cholestasis represents one out of three types of drug induced liver injury (DILI), which comprises a major challenge in drug development. In this study we applied a two-class classification scheme based on k-nearest neighbors in order to predict cholestasis, using a set of 93 two-dimensional (2D) physicochemical descriptors and predictions of selected hepatic transporters' inhibition (BSEP, BCRP, P-gp, OATP1B1, and OATP1B3). In order to assess the potential contribution of transporter inhibition, we compared whether the inclusion of the transporters' inhibition predictions contributes to a significant increase in model performance in comparison to the plain use of the 93 2D physicochemical descriptors. Our findings were in agreement with literature findings, indicating a contribution not only from BSEP inhibition but a rather synergistic effect deriving from the whole set of transporters. The final optimal model was validated via both 10-fold cross validation and external validation. It performs quite satisfactorily resulting in 0.686 ± 0.013 for accuracy and 0.722 ± 0.014 for area under the receiver operating characteristic curve (AUC) for 10-fold cross-validation (mean ± standard deviation from 50 iterations).
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.