ArticleInternational journal of molecular sciences2020
In Silico Prediction of Intestinal Permeability by Hierarchical Support Vector Regression.
Article in International journal of molecular sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Cross-Database Characterization of Flavonoids and Phenolic Acids: Integrating Drug-likeness Metrics, Molecular Interactions, and Dietary Sources.Molecules (Basel, Switzerland) · 2026Article
- Model-Informed Drug Development: In Silico Assessment of Drug Bioperformance following Oral and Percutaneous Administration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- In Silico Prediction of Skin Permeability Using a Two-QSAR Approach.Pharmaceutics · 2022Article
- Development of a Hierarchical Support Vector Regression-Based In Silico Model for Caco-2 Permeability.Pharmaceutics · 2021Article
- Article
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.
Funding
Abstract
The vast majority of marketed drugs are orally administrated. As such, drug absorption is one of the important drug metabolism and pharmacokinetics parameters that should be assessed in the process of drug discovery and development. A nonlinear quantitative structure-activity relationship (QSAR) model was constructed in this investigation using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to render the extremely complicated relationships between descriptors and intestinal permeability that can take place through various passive diffusion and carrier-mediated active transport routes. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (
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.