ArticleJournal of computer-aided molecular design2026
QSAR, molecular dynamics, and biological evaluation of novel myeloperoxidase inhibitors via ligand-based pharmacophore modeling as potential anticancer agents.
Article in Journal of computer-aided molecular design, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Development and In Vitro Evaluation of Atorvastatin and Rutin Co-Loaded Nanoliposomes for Enhanced Anti-Inflammatory and Cytotoxic Efficacy.International journal of molecular sciences · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Myeloperoxidase (MPO) has shown promise as a therapeutic target due to its critical role in inflammatory mechanisms and cancer progression. Despite extensive research on MPO inhibitors, the lack of integrated computational-experimental workflows constrains the efficient identification and validation of biologically relevant candidates. Hence, in this study, a ligand-based pharmacophore model of MPO inhibitors was developed to identify crucial molecular features required for inhibition. A quantitative structure-activity relationship (QSAR) model was built using the Genetic Function Approximation (GFA) algorithm, and the model statistics were found to be statistically significant (R
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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.