ArticleHeliyon2019
QSAR study of anti-Human African Trypanosomiasis activity for 2-phenylimidazopyridines derivatives using DFT and Lipinski's descriptors.
Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Synthesis and characterization of novel bis(thiosemicarbazone) complexes and investigation of their acetylcholinesterase and glutathione S-transferase activities with in silico and in vitro studies.Molecular diversity · 2025Article
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- QSAR, ADMET In Silico Pharmacokinetics, Molecular Docking and Molecular Dynamics Studies of Novel Bicyclo (Aryl Methyl) Benzamides as Potent GlyT1 Inhibitors for the Treatment of Schizophrenia.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Design of novel benzimidazole derivatives as potential α-amylase inhibitors using QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation studies.Journal of molecular modeling · 2022Article
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- QSAR study of unsymmetrical aromatic disulfides as potent avian SARS-CoV main protease inhibitors using quantum chemical descriptors and statistical methods.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2021Article
- Modeling antimalarial and antihuman African trypanosomiasis compounds: a ligand- and structure-based approaches.Molecular diversity · 2020Article
- Article
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Authors and funding
9 authors.
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Abstract
The quantitative structure-activity relationship (QSAR) of sixty 2-phenylimidazopyridines derivatives with anti-Human African Trypanosomiasis (anti-HAT) activity has been studied by using the density functional theory (DFT) and statistical methods. Becke's three-parameter hybrid method and the Lee-Yang-Parr B3LYP functional employing 6-31G(d) basis set are used to calculate quantum chemical descriptors using Gaussian 03W software, and the five Lipinski's parameters were calculated using ChemOffice software. In order to obtain robust and reliable QSAR model, the original dataset was randomly divided into training and prediction sets comprising 48 and 12 compounds, respectively. An optimal model for the training set with significant statistical quality was established. The same model was further applied to predict pEC
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