ArticleHeliyon2020
QSAR and molecular docking studies of 1,3-dioxoisoindoline-4-aminoquinolines as potent antiplasmodium hybrid compounds.
Article in Heliyon, 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
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Who cites it
6 citing papers in PubMed.
- Computational Identification of Potential Novel Allosteric IHF Inhibitors Using QSAR Modeling to Inhibit Plasmid-Mediated Antibiotic Resistance.International journal of molecular sciences · 2026Article
- 2D/3D-QSAR Model Development Based on a Quinoline Pharmacophoric Core for the Inhibition ofPharmaceuticals (Basel, Switzerland) · 2024Article
- Ligand based-design of potential schistosomiasis inhibitors through QSAR, homology modeling, molecular dynamics, pharmacokinetics, and DFT studies.Journal of Taibah University Medical Sciences · 2024Article
- In silico analysis of noscapine compounds as anti-tumor agents targeting the tubulin receptor.Journal of Taibah University Medical Sciences · 2023Article
- QSAR Studies, Molecular Docking, Molecular Dynamics, Synthesis, and Biological Evaluation of Novel Quinolinone-Based Thiosemicarbazones againstAntibiotics (Basel, Switzerland) · 2022Article
- Antimalarial Drug Predictions Using Molecular Descriptors and Machine Learning against Plasmodium Falciparum.Biomolecules · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantitative structure-activity relationships (QSAR) provides a model that link biological activities of compounds to thier chemical stuctures and molecular docking study reveals the interaction between drug and its target enzyme. These studies were conducted on 1,3-dioxoisoindoline-4-aminoquinolines with the aim of producing a model that could be used to design highly potent antiplasmodium. The compounds were first optimized using Density Functional Theory (DFT) with basis set B3LYP/6-31G∗ then their descriptors calculated. Genetic Function Algorithm (GFA) was used to select descriptors and build the model. One of the four models generated was found to be the best having internal and external squared correlation coefficient (
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Registered trials
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