Computational identification of potential VEGFR2 inhibitors using integrated pharmacophore modeling, QSAR analysis, molecular docking, dynamics simulation, and virtual screening followed by in vitro bioassays
Aug 2026· Journal of Computer-Aided Molecular Design· Vol 40· 0 citations· 61 references
Medicine
TL;DR
Several structurally novel compounds are identified as promising potential VEGFR2 inhibitors and supports the effectiveness of integrating pharmacophore modeling, QSAR analysis, molecular docking, and experimental validation for anticancer drug discovery.
The findings from molecular docking, 500-ns molecular dynamics simulations, MM-GBSA calculations, and alanine scanning analyses collectively corroborate a stable binding mode of BTB11556 within the MPO active site, support further investigation of BTB11556 as a candidate compound associated with MPO-targeted therapeutic strategies.
Maysoon Raed Alnajdawi, H. Wahab, Belal Alnajjar et al.· Journal of Computer-Aided Mo...· 0 citations
This research developed a promising lead molecule (HIT1) as a therapeutic approach for ER-positive breast cancer through pharmacophore model-based drug design of thiazine derivative targeting ERα via pharmacophore model-based drug design.
M. S. Sanjeev, Bhim Singh, Kailash Jangid et al.· Journal of Molecular Graphic...· 0 citations
This study highlights natural diterpenoids and coumarin glycosides as promising scaffolds for caspase-1 inhibition and demonstrates that integrating QSAR modeling with structure-based approaches provides an efficient strategy for discovering potential anti-inflammatory drug candidates.
Yusuf Şeflekçi, Alper Yılmaz, Abdulilah Ece· Molecules· 0 citations
Background: Chronic diabetic complications develop through the important role of aldose reductase (AR), a key enzyme in the polyol pathway. Clearly, it is important to identify potent AR inhibitors with better PK properties for treatment of diabetes-associated complications.
Purpose: The purpose of this study was to discover novel 1,3,4-thiadiazole derivatives as aldose reductase inhibitors using an integrated computational drug discovery approach.
Methods: The dataset consisted of 30 reported 1,3,4-thiadiazole derivatives, which were analysed by the pharmacophore modelling, atom-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, structure-activity relationship (SAR) analysis, R-group enumeration, virtual screening and ADMET prediction methods. Using the best pharmacophore model (AHHRR_1), a validated 3D-QSAR model was developed, and 1,419 new derivatives were designed. These compounds were then further optimised for binding interactions and pharmacokinetic parameters with the top-ranked ones, including the designed derivative PD01.
Results: The optimised pharmacophore and 3D-QSAR models were able to recognise the crucial structural elements that are essential for AR inhibition. Activity increased with the hydrophobic and electron-withdrawing groups, while the bulky polar groups were responsible for decreased activity. Docking studies showed that compounds 04 (-10.178 kcal/mol), 01 (-10.081 kcal/mol), 02 (-10.050 kcal/mol), 10 (-9.977 kcal/mol), and 11 (-9.672 kcal/mol) exhibited stronger binding than Epalrestat (-8.182 kcal/mol). The highest docking score was obtained for PD01 (-10.605 kcal/mol), which had strong hydrogen-bond, hydrophobic, π-π stacking, π-cation and halogen-bond interactions. The ADMET analysis showed good drug-likeness and good oral absorption.
Conclusion: The integrated computational workflow has concluded that PD01 is the most promising lead candidate with excellent binding affinity, a favourable interaction pattern and desirable ADMET properties. The results suggest that the scaffold 1,3,4-thiadiazole is a promising structural template for designing new generation aldose reductase inhibitors for diabetic complications.
Priya Devi, Debarshi Mondal, Shalini Sharma et al.· Journal of Pharmaceutical Te...· 0 citations
The integrated computational approach identified ZINC000000867238 as a potent and stable CCR5 inhibitor candidate, warranting further in vitro and in vivo validation as a potential HIV-1 entry blocker.
A. Sathish Kumar, Estari Mamidala· Journal of Receptor and Sign...· 0 citations
The MGC803 cell line is a human gastric cancer model frequently used in cancer research. In this context, a combined in silico approach including 3D-QSAR modeling, ADMET analysis, network pharmacology, docking, molecular dynamics and ligand transport evaluations, was applied to design new antiproliferative molecules. A robust 3D-QSAR model with high predictive capacity (R² and Q²) was developed and used to design new compounds (PR1–PR4). After an ADMET screening, the putative biological targets of the non-toxic compounds were predicted using PharmMapper. A network pharmacology analysis identified several hub genes, of which HSP90AA1 had the highest degree value. Given its central role in stabilizing multiple oncogenic proteins involved in gastric cancer progression, as well as its suitability for structure-based studies, HSP90AA1 was selected for molecular docking and molecular dynamics simulations. In addition, molecular docking was performed on HSP90AA1 protein (1YET) in complex with the designed molecules (PR1-PR4), and their predicted binding behaviors were compared to both the most active molecule (M34) and the reference drug, geldanamycin. These results demonstrate a high predicted binding affinity and remarkable interaction profiles within the active site of the HSP90AA1. To further evaluate the dynamic stability of these complexes, we performed molecular dynamics simulations over a 100 ns period, thus confirming stable attachment modes and durable contact networks. The MM-PBSA approach demonstrated favorable binding free energies between the chosen PR4 ligand and the 1YET protein (-26.47 ± 2.89 kcal/mol). Finally, the ligand transport study showed that the PR4 ligand easily crosses tunnels 1 and 2 with optimal theoretical transport dynamics compared to the reference drug, geldanamycin (GA). This comprehensive computational method underlines the diverse potential of the examined molecules, identifying the most promising candidates for subsequent experimental validation against gastric cancer.
L. Naanaai, Abdellah El Aissouq, Yassine El Allouche et al.· Beni-Suef University Journal...· 0 citations
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