ArticleJournal of computer-aided molecular design2025
Computational QSAR study of novel 2-aminothiazol-4(5H)-one derivatives as 11β-HSD1 inhibitors.
Article in Journal of computer-aided molecular design, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Article
- Assessing ZnScientific reports · 2026Article
- Identification of PI3K alpha inhibitors through large-scale virtual screening and integrated molecular modeling, biophysical characterization, and ADMET profiling.Scientific reports · 2026Article
- A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts.Scientific reports · 2026Article
- Peptide-Based Therapeutics for Alzheimer's Disease: Medicinal Chemistry, AI-Guided Computational Design, and Blood-Brain Barrier Delivery.Drug design, development and therapy · 2026Review
- Swarm Intelligence in Drug Discovery Applications: Unlocking Deeper Insights on the Identification and Optimization of Potential Drug Candidates.Drug design, development and therapy · 2026Review
- New Derivatives of 2-(Cyclohexylamino)thiazol-4(5International journal of molecular sciences · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
This research aims to develop a predictive model to support the design of new 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1) inhibitors based on the pseudothiohydantoin scaffold, offering potential for novel treatments of metabolic disorders. The Quantitative Structure-Activity Relationship (QSAR) analysis was performed on 56 2-aminothiazol-4(5h)-one derivatives, for which the 11β-HSD1 inhibitory activity was previously reported. Gaussian software was employed for geometry optimization, while Dragon software was used to calculate the molecular descriptors. The study used an Artificial Neural Network (ANN) algorithm for regression analysis between the top ten preselected descriptors and the activity of the studied analogs. A predictive model was developed using a network architecture 10-11-1 with a Broyden-Fletcher-Goldfarb-Shanno learning algorithm. The model's reliability was supported through cross-validation and y-randomization strategies. The model exhibited high accuracy with a determination coefficient (R
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.