ArticleNAM journal2025
QSAR, molecular docking, molecular dynamics and DFT-based design of novel Quinoline-2-yl (piperazin-1-yl) inhibitors of hepatitis C virus.
Article in NAM journal, 2025. 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.
- Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver cirrhosis and hepatocellular cancer are brought on by the hepatitis C virus (HCV). Since the virus's discovery, significant therapeutic advancements have been made. Recent studies have demonstrated the growing importance of plant compounds in the creation of novel, efficient, and reasonably priced anti-Hepatitis C virus therapies. The present study involved a thorough analysis of 35 Quinoline-2-yl (piperazin-1-yl) compounds. These compounds were computationally analyzed using in-silico methods like 2D-3D QSAR modeling and molecular docking, and their results were verified through the use of Density Functional Theory (DFT) calculations and ADMET characteristics assessment. The inhibitors were optimized using DFT based on a notion of B3LYP/6-31G* levels. The genetic function algorithm (GFA) was utilized to create the QSAR models. The best model was chosen based on its statistical fitness using the subsequent measurement parameters: R
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Registered trials
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