Unlocking the Potential of RGD-Conjugated Gold Nanoparticles: A New Frontier in Targeted Cancer Therapy, Imaging, and Metastasis Inhibition

Journal of Materials Chemistry B, Год журнала: 2024, Номер 12(42), С. 10786 - 10817

Опубликована: Янв. 1, 2024

The review highlights the potential of RGD-conjugated AuNPs in cancer diagnosis and treatment, including breast cancer. It emphasizes need for further research to fully realize this technology’s inspire future investigations.

Язык: Английский

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Structural characterization and in silico evaluation of bioactive compounds from Rhus cotinus (Syn. Cotinus coggygria) roots as potential EGFR inhibitors for brain Cancer

Results in Chemistry, Год журнала: 2025, Номер unknown, С. 102101 - 102101

Опубликована: Фев. 1, 2025

Язык: Английский

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Evaluation of Machine Learning Methods for Identifying Carbonic Anhydrase-II Inhibitors as Drug Candidates for Glaucoma

Malacca Pharmaceutics, Год журнала: 2025, Номер 3(1), С. 32 - 41

Опубликована: Март 4, 2025

Glaucoma is a leading cause of irreversible blindness, primarily managed by lowering intraocular pressure (IOP). Carbonic Anhydrase-II (CA-II) inhibitors play crucial role in this treatment reducing aqueous humor production. However, existing CA-II often suffer from poor selectivity, side effects, and limited bioavailability, highlighting the need for more efficient targeted drug discovery approaches. This study uses machine learning-driven Quantitative Structure-Activity Relationship (QSAR) modeling to predict inhibition based on molecular descriptors, significantly enhancing screening efficiency over traditional experimental methods. By evaluating multiple learning models, including Support Vector Machine, Gradient Boosting, Random Forest, we identify SVM as most effective classifier, achieving highest accuracy (83.70%) F1-score (89.36%). Class imbalance remains challenging despite high sensitivity, necessitating further improvements through resampling hyperparameter optimization. Our findings underscore potential learning-based virtual accelerating inhibitor identification advocate integrating AI-driven approaches with techniques. Future directions include deep enhancements hybrid learning-docking frameworks improve prediction facilitate development potent selective glaucoma treatments.

Язык: Английский

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Machine learning and cheminformatics-based Identification of lichen-derived compounds targeting mutant PBP4R200L in Staphylococcus aureus

Molecular Diversity, Год журнала: 2025, Номер unknown

Опубликована: Фев. 15, 2025

Язык: Английский

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Crystalline Liquiritigenin and Liquiritin: Structural Characterization, Molecular Docking Studies, and Anti-Amyloid-β Evaluation in Caenorhabditis elegans

ACS Omega, Год журнала: 2025, Номер 10(7), С. 7112 - 7119

Опубликована: Фев. 16, 2025

Two new crystalline compounds, named [LG·H2O]n (1; LG = liquiritigenin) and [LQ·C2H5OH·H2O]n (2; LQ liquiritin), have been synthesized structurally characterized by single-crystal powder X-ray diffraction, thermogravimetric analyses (TGA), nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), infrared spectra (IR). 1 2 crystallize in space groups Pna21 P212121, respectively. In the structure of 1, liquiritigenin water molecules are connected hydrogen bonds for construction a novel 3,5-connected network topology with point symbol (63)(67·83), which each molecule acts as 5-connected 3-connected node, Both reduce amyloid-β-induced toxicity Caenorhabditis elegans (CL4176 strain) improving expression level SOD. Gene studies RT-qPCR indicate upregulation skn-1 sod-3 while downregulation daf-16 hsf-1 C. elegans. Molecular docking that combine well vascular endothelial growth factor A (VEGFA), free binding energies calculated to be −6.7 −7.9 kcal·mol–1, Moreover, anti-amyloid-β ability amorphous or has studied.

Язык: Английский

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In-silico investigation integrated with machine learning to identify potential inhibitors targeting AKT2: Key driver of cancer cell progression and metastasis

Computer Methods and Programs in Biomedicine, Год журнала: 2025, Номер unknown, С. 108793 - 108793

Опубликована: Апрель 1, 2025

Язык: Английский

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Design of Novel Thiazole‐based Schiff Analogs as α‐Amylase Inhibitors Using 3D‐QSAR, ADME‐Tox, Molecular Docking, Molecular Dynamics, Biological Efficacy, and Retrosynthesis

ChemistrySelect, Год журнала: 2024, Номер 9(47)

Опубликована: Дек. 1, 2024

Abstract This study enabled us to develop new analogs of the Schiff thiazole base with high inhibitory activity against α‐amylase enzyme as effective anti‐diabetic drug candidates. To this end, we used virtual screening methods such 3D‐QSAR, molecular docking, ADMET properties, dynamics simulation, biological efficacy, and retrosynthesis on selected derivatives. The results 3D‐QSAR modeling showed that CoMSIA_DH model has excellent predictive ability (Q 2 = 0.71, R train 0.978, test 0.987, SEE 0.072). Using template (17), designed three ligands activities enzyme. predictions for molecules met Lipinski's rule pharmacokinetic profiles. Ligands were anchored in α‐amylase's active site, showing good binding affinities. docking stability receptor confirmed through simulations. CaverDock program was utilized identify tunnels which are most likely migrate from site surface, thereby determining efficacy target compounds. found compound B1 be effective, using retrosynthesis, a pathway synthesis these therapeutic prospects identified.

Язык: Английский

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Synthesis, Crystal Structure, DFT Calculations, Detection Limit, Solvent Effect on Nonlinear Optical Properties, and Molecular Docking of New Schiff-Base Cu(II) Complex

Journal of Molecular Structure, Год журнала: 2024, Номер unknown, С. 141178 - 141178

Опубликована: Дек. 1, 2024

Язык: Английский

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Flavonoids as potential SARS-CoV-2 main protease inhibitors: In vitro activity evaluation, molecular docking, 3D-QSAR investigation and synthesis

Journal of Molecular Structure, Год журнала: 2024, Номер unknown, С. 140286 - 140286

Опубликована: Окт. 1, 2024

Язык: Английский

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